NET Wiki - User contributions [en]

Theses and Projects

2018-05-17T12:54:59Z

Dkoll: /* Completed Topics */

== Open Theses and Student Project Topics ==

The Computer Networks Group is always looking for motivated students to work on various topics. If you are interested in any of the projects below, or if you have other ideas and are willing to work with us, please don't hesitate to [mailto:net@informatik.uni-goettingen.de contact us].

* (B) Bachelor thesis
* (M) Master thesis
* (P) Student project



=== Software Defined Networks (SDN) ===

* '''New!''' Including a Graph Database engine into an SDN Controller. (B/M/P) [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat] [https://wiki.net.informatik.uni-goettingen.de/wiki/Gavel details]

* '''New!''' [[SDN Simulator: Implementation and validation of NS-3 or OMNET++ based SDN Simulator ]] (B/M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]

* '''New!''' [[Open SDN Testbed: Realize the SDN testbed and automation of network topologies using the EU GEANT Testbed services ]] (B/M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]

* '''New!''' A graph database tuning. (B/M/P) [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat] [https://wiki.net.informatik.uni-goettingen.de/wiki/Gavel details]

* '''New!''' Implementing more Gavel application by exploiting Graph algorithms. (B/M/P) [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat] [https://wiki.net.informatik.uni-goettingen.de/wiki/Gavel details]

* '''New!''' Demonstrating Security Vulnerabilities of SDN Controller (ONOS) (B/M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/abhinandan_s_prasad Abhinandan S Prasad]

* '''New!''' Modeling Performance of SDN topologies using Queuing theory (B/M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/abhinandan_s_prasad Abhinandan S Prasad]

* '''New!''' Implementation of sFlow for ONOS (Migrating existing code to new ONOS version (B/P) contact with [http://www.net.informatik.uni-goettingen.de/people/abhinandan_s_prasad Abhinandan S Prasad]

* '''New!''' Implementation of virtual switch using libfluid Openflow C++ library (B/P) contact with [http://www.net.informatik.uni-goettingen.de/people/abhinandan_s_prasad Abhinandan S Prasad]

===Network Function Virtualization (NFV) ===
* '''New!''' [[Management and Orchestration: Design and Implementation of NFV Management and Orchestration Layer with OpenStack, based on the ESTI NFVI-MANO and OPNFV frameworks.]] (M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]

* '''New!''' [[NSH Routing: Implementation of Network Service Headers to realize the service chain by steering traffic across the VNFs.]] (M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]

* '''New!''' [[VNF components: Implementation of Virtual Network Functions like Proxy Engines, Firewall, IDS and IPS, on top of OpenNetVM, Docker engines using the available open source tools. ]] (M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]

=== Future Internet architecture ===

* if you are interested in other topics in this area please get in contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]

* '''New!''' [[Video Delivery: Implementation and validation of SAID, a congestion control protocol for Multicast (A joint project with CISCO) ]] (B/M/P)

* '''New!''' [[IoT: Implementation of a Service using Named Function Networking for supporting essential functions of IoT]] (B/M/P)

* '''New!''' [[Interactive Video: Implementing a interactive video application in ICN using the NeMoI architecture]] (B/M/P)

* '''New!''' [[Gateway: Extend Named Function Networking to support protocol Translation]] (B/M/P)

* [[Network Management: Information Centric Networking (ICN) based solution for Network Management]] (B/M/P)

* '''New!''' [[Develop a web server for displaying statistics using restful service]] (B/M/P)

* '''New!''' [[A video resolver for android application]] (B/M/P)

* [[Infrastructureless, Delay Tolerant Network in different Context: Internet of Things, Emergency, Mobile Social Networks, Pervasive Computing]] (B/M/P) (currently unavailable)

* [[Disaster Recovery: Implementation and evaluation of Mobile phone based Information Centric Networking (ICN) solution for support during disasters]] (B/M/P) (currently unavailable)

* [[Wireless mesh networks/vehicular networks/wireless sensor networks: Information Centric Networking (ICN) based solution]] (B/M/P) (currently unavailable)

=== Data Crawling and analysis ===

* [[Large scale distributed Data crawling and analysis of a popular web service]] (B/M/P)

* '''New!''' [[Data crawling and analysis of Twitter]] (P) ([http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao])

* if you are interested in other topics in this area please get in contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]

=== Massive Data Mining and Recommender System===

* [[Data Mining of the Web : User Behavior Analysis]] (B/M/P) [http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]

* [[Building the Genealogy for Researchers]] (B/M/P)[http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]

* [[Recommender System Design]] (B/M/P)[http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]

* if you are interested in other topics in this area please get in contact with [http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]

=== Social Networking ===
* '''New!''' [[Goettingen Assistant: Android App Development]] (P) ([http://www.net.informatik.uni-goettingen.de/people/shichang_ding Shichang Ding])
* [[Topic prediction in online social networks]] (B/M/P)([http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang])
* [[Mining emotion patterns in online social networks]] (B/M/P)([http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang])
* Mining human mobility pattern from intra-city traffic data (B/M/P) ([http://www.net.informatik.uni-goettingen.de/people/shichang_ding Shichang Ding])

=== Information Centric Networking (ICN) ===
* ICN over GTS: exploit Geant Testbed Service to build configurable ICN testbeds (B/M/P) ([http://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto])
* ICNProSe: ICN-based Proximity Discovery Services (B/M/P) ([http://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto])

== Ongoing Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Topic advisor'''
|{{Hl2}} |'''Initial readings'''
|{{Hl2}} |'''Description'''
|{{Hl2}} |'''Student'''

|-
|Sentiment Analysis (Student project)
|[http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]
|
|
| Assigned to Beatrice Kateule
|-
| Analysis of Business Transitions: A Case Study of Yelp (Bachelor Thesis)
|[http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]
|
|
| Assigned to Marcus Thomas Khalil
|-
| Understanding Group Patterns in Q&A Services (Bachelor Thesis)
|[http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]
|
|
| Assigned to Jonas Koopmann
|-
| COPSS-lite : Lightweight ICN Based Pub/Sub for IoT Environments (Master Thesis)
| [http://www.net.informatik.uni-goettingen.de/people/sripriya%20srikant_adhatarao Sripriya]
|
|
| Assigned to Haitao Wang
|-
| A ICN Gateway for IoT (Bachelor Thesis)
| [http://www.net.informatik.uni-goettingen.de/people/sripriya%20srikant_adhatarao Sripriya]
|
|
| Assigned to Janosch Ruff
|-
|}

== Completed Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Topic advisor'''
|{{Hl2}} |'''Initial readings'''
|{{Hl2}} |'''Description'''
|{{Hl2}} |'''Student'''

|-
| Personalized Recommender System Design (Master thesis Project)
| [http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]
|
| Build a personalized context-aware recommender system for customers according to their own interest.
| Completed by Haile Misgna
|-
| Emotion Patterns Analysis in OSNs (Bachelor thesis Project)
| [http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang],[http://www.net.informatik.uni-goettingen.de/people/xu_chen Xu Chen]
|
| We aim to study the emotion patterns in the Twitter service and predict the future emotion status of users.
| Completed by Stefan Peters
|-
| Implementation of a pub/sub system (Student project)
| [http://www.net.informatik.uni-goettingen.de/people/jiachen_chen Jiachen Chen] [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]
|
| The aim of the work is to show how application layer intelligence cupled with network layer pub/sub can be beneficial to both users as well as network operators
| Completed by Sripriya
|-
| Large Scale Distributed Natural Language Document Generation System (Student project at IBM)
| [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]
|
| The work was done at IBM
| Completed by Eeran Maiti
|-
| Investigate real time streaming tools for large scale data processing (Student project)
| [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]
|
| The aim of the work is to compare real time streaming tools.
| Completed by Ram
|-
| Software-Defined Networking and Network Operating System (Student project)
| [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]
|
| SDN based ntwork operating system
| Completed by Rasha
|-
| GEMSTONE goes Mobile (BSc Thesis/Student Project)
| [http://user.informatik.uni-goettingen.de/~dkoll David Koll]
|
| Portation of a Decentralized Online Social Network to the Android Platform
| Completed by Fabien Mathey and improved by Eeran Maiti
|-
| Transitioning of Social Graphs between Multiple Online Social Networks (BSc Thesis)
| [http://user.informatik.uni-goettingen.de/~dkoll David Koll]
|
| Portation of friendship graphs between different Online Social Networks
| Completed by Kai-Stephan Jacobsen
|-
| Prevention and Mitigation of (D)DoS Attacks in Enterprise Environments (BSc Thesis)
| [http://user.informatik.uni-goettingen.de/~dkoll David Koll]
|
| An analysis of enterprise infrastructures and their vulnerarbility towards attacks from the outside.
| Completed by David Kelterer
|-
| Sybils in Disguise: An Attacker View on OSN-based Sybil Defenses (Student Project and MSc Thesis)
| [http://user.informatik.uni-goettingen.de/~dkoll David Koll]
|
| An analysis of fake detection approaches in social networks.
| Completed by Martin Schwarzmaier
|-
| Design and Implementation of a distributed OSN on Home Gateways (Student project and Master's Thesis)
|[http://user.informatik.uni-goettingen.de/~dkoll David Koll]
|
|
| Completed by Dieter Lechler
|-
|}

* For a full list of older topics please go [http://www.net.informatik.uni-goettingen.de/student_projects here].
</noinclude>

Theses and Projects

2018-05-17T12:54:35Z

Dkoll: /* Ongoing Topics */

== Open Theses and Student Project Topics ==

The Computer Networks Group is always looking for motivated students to work on various topics. If you are interested in any of the projects below, or if you have other ideas and are willing to work with us, please don't hesitate to [mailto:net@informatik.uni-goettingen.de contact us].

* (B) Bachelor thesis
* (M) Master thesis
* (P) Student project



=== Software Defined Networks (SDN) ===

* '''New!''' Including a Graph Database engine into an SDN Controller. (B/M/P) [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat] [https://wiki.net.informatik.uni-goettingen.de/wiki/Gavel details]

* '''New!''' [[SDN Simulator: Implementation and validation of NS-3 or OMNET++ based SDN Simulator ]] (B/M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]

* '''New!''' [[Open SDN Testbed: Realize the SDN testbed and automation of network topologies using the EU GEANT Testbed services ]] (B/M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]

* '''New!''' A graph database tuning. (B/M/P) [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat] [https://wiki.net.informatik.uni-goettingen.de/wiki/Gavel details]

* '''New!''' Implementing more Gavel application by exploiting Graph algorithms. (B/M/P) [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat] [https://wiki.net.informatik.uni-goettingen.de/wiki/Gavel details]

* '''New!''' Demonstrating Security Vulnerabilities of SDN Controller (ONOS) (B/M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/abhinandan_s_prasad Abhinandan S Prasad]

* '''New!''' Modeling Performance of SDN topologies using Queuing theory (B/M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/abhinandan_s_prasad Abhinandan S Prasad]

* '''New!''' Implementation of sFlow for ONOS (Migrating existing code to new ONOS version (B/P) contact with [http://www.net.informatik.uni-goettingen.de/people/abhinandan_s_prasad Abhinandan S Prasad]

* '''New!''' Implementation of virtual switch using libfluid Openflow C++ library (B/P) contact with [http://www.net.informatik.uni-goettingen.de/people/abhinandan_s_prasad Abhinandan S Prasad]

===Network Function Virtualization (NFV) ===
* '''New!''' [[Management and Orchestration: Design and Implementation of NFV Management and Orchestration Layer with OpenStack, based on the ESTI NFVI-MANO and OPNFV frameworks.]] (M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]

* '''New!''' [[NSH Routing: Implementation of Network Service Headers to realize the service chain by steering traffic across the VNFs.]] (M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]

* '''New!''' [[VNF components: Implementation of Virtual Network Functions like Proxy Engines, Firewall, IDS and IPS, on top of OpenNetVM, Docker engines using the available open source tools. ]] (M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]

=== Future Internet architecture ===

* if you are interested in other topics in this area please get in contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]

* '''New!''' [[Video Delivery: Implementation and validation of SAID, a congestion control protocol for Multicast (A joint project with CISCO) ]] (B/M/P)

* '''New!''' [[IoT: Implementation of a Service using Named Function Networking for supporting essential functions of IoT]] (B/M/P)

* '''New!''' [[Interactive Video: Implementing a interactive video application in ICN using the NeMoI architecture]] (B/M/P)

* '''New!''' [[Gateway: Extend Named Function Networking to support protocol Translation]] (B/M/P)

* [[Network Management: Information Centric Networking (ICN) based solution for Network Management]] (B/M/P)

* '''New!''' [[Develop a web server for displaying statistics using restful service]] (B/M/P)

* '''New!''' [[A video resolver for android application]] (B/M/P)

* [[Infrastructureless, Delay Tolerant Network in different Context: Internet of Things, Emergency, Mobile Social Networks, Pervasive Computing]] (B/M/P) (currently unavailable)

* [[Disaster Recovery: Implementation and evaluation of Mobile phone based Information Centric Networking (ICN) solution for support during disasters]] (B/M/P) (currently unavailable)

* [[Wireless mesh networks/vehicular networks/wireless sensor networks: Information Centric Networking (ICN) based solution]] (B/M/P) (currently unavailable)

=== Data Crawling and analysis ===

* [[Large scale distributed Data crawling and analysis of a popular web service]] (B/M/P)

* '''New!''' [[Data crawling and analysis of Twitter]] (P) ([http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao])

* if you are interested in other topics in this area please get in contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]

=== Massive Data Mining and Recommender System===

* [[Data Mining of the Web : User Behavior Analysis]] (B/M/P) [http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]

* [[Building the Genealogy for Researchers]] (B/M/P)[http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]

* [[Recommender System Design]] (B/M/P)[http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]

* if you are interested in other topics in this area please get in contact with [http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]

=== Social Networking ===
* '''New!''' [[Goettingen Assistant: Android App Development]] (P) ([http://www.net.informatik.uni-goettingen.de/people/shichang_ding Shichang Ding])
* [[Topic prediction in online social networks]] (B/M/P)([http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang])
* [[Mining emotion patterns in online social networks]] (B/M/P)([http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang])
* Mining human mobility pattern from intra-city traffic data (B/M/P) ([http://www.net.informatik.uni-goettingen.de/people/shichang_ding Shichang Ding])

=== Information Centric Networking (ICN) ===
* ICN over GTS: exploit Geant Testbed Service to build configurable ICN testbeds (B/M/P) ([http://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto])
* ICNProSe: ICN-based Proximity Discovery Services (B/M/P) ([http://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto])

== Ongoing Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Topic advisor'''
|{{Hl2}} |'''Initial readings'''
|{{Hl2}} |'''Description'''
|{{Hl2}} |'''Student'''

|-
|Sentiment Analysis (Student project)
|[http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]
|
|
| Assigned to Beatrice Kateule
|-
| Analysis of Business Transitions: A Case Study of Yelp (Bachelor Thesis)
|[http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]
|
|
| Assigned to Marcus Thomas Khalil
|-
| Understanding Group Patterns in Q&A Services (Bachelor Thesis)
|[http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]
|
|
| Assigned to Jonas Koopmann
|-
| COPSS-lite : Lightweight ICN Based Pub/Sub for IoT Environments (Master Thesis)
| [http://www.net.informatik.uni-goettingen.de/people/sripriya%20srikant_adhatarao Sripriya]
|
|
| Assigned to Haitao Wang
|-
| A ICN Gateway for IoT (Bachelor Thesis)
| [http://www.net.informatik.uni-goettingen.de/people/sripriya%20srikant_adhatarao Sripriya]
|
|
| Assigned to Janosch Ruff
|-
|}

== Completed Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Topic advisor'''
|{{Hl2}} |'''Initial readings'''
|{{Hl2}} |'''Description'''
|{{Hl2}} |'''Student'''

|-
| Personalized Recommender System Design (Master thesis Project)
| [http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]
|
| Build a personalized context-aware recommender system for customers according to their own interest.
| Completed by Haile Misgna
|-
| Emotion Patterns Analysis in OSNs (Bachelor thesis Project)
| [http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang],[http://www.net.informatik.uni-goettingen.de/people/xu_chen Xu Chen]
|
| We aim to study the emotion patterns in the Twitter service and predict the future emotion status of users.
| Completed by Stefan Peters
|-
| Implementation of a pub/sub system (Student project)
| [http://www.net.informatik.uni-goettingen.de/people/jiachen_chen Jiachen Chen] [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]
|
| The aim of the work is to show how application layer intelligence cupled with network layer pub/sub can be beneficial to both users as well as network operators
| Completed by Sripriya
|-
| Large Scale Distributed Natural Language Document Generation System (Student project at IBM)
| [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]
|
| The work was done at IBM
| Completed by Eeran Maiti
|-
| Investigate real time streaming tools for large scale data processing (Student project)
| [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]
|
| The aim of the work is to compare real time streaming tools.
| Completed by Ram
|-
| Software-Defined Networking and Network Operating System (Student project)
| [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]
|
| SDN based ntwork operating system
| Completed by Rasha
|-
| GEMSTONE goes Mobile (BSc Thesis/Student Project)
| [http://user.informatik.uni-goettingen.de/~dkoll David Koll]
|
| Portation of a Decentralized Online Social Network to the Android Platform
| Completed by Fabien Mathey and improved by Eeran Maiti
|-
| Transitioning of Social Graphs between Multiple Online Social Networks (BSc Thesis)
| [http://user.informatik.uni-goettingen.de/~dkoll David Koll]
|
| Portation of friendship graphs between different Online Social Networks
| Completed by Kai-Stephan Jacobsen
|-
| Prevention and Mitigation of (D)DoS Attacks in Enterprise Environments (BSc Thesis)
| [http://user.informatik.uni-goettingen.de/~dkoll David Koll]
|
| An analysis of enterprise infrastructures and their vulnerarbility towards attacks from the outside.
| Completed by David Kelterer
|-
| Sybils in Disguise: An Attacker View on OSN-based Sybil Defenses (Student Project and MSc Thesis)
| [http://user.informatik.uni-goettingen.de/~dkoll David Koll]
|
| An analysis of fake detection approaches in social networks.
| Completed by Martin Schwarzmaier
|-
|}

* For a full list of older topics please go [http://www.net.informatik.uni-goettingen.de/student_projects here].
</noinclude>

Theses and Projects

2018-04-12T08:41:53Z

Dkoll:

== Open Theses and Student Project Topics ==

The Computer Networks Group is always looking for motivated students to work on various topics. If you are interested in any of the projects below, or if you have other ideas and are willing to work with us, please don't hesitate to [mailto:net@informatik.uni-goettingen.de contact us].

* (B) Bachelor thesis
* (M) Master thesis
* (P) Student project



=== Software Defined Networks (SDN) ===

* '''New!''' Including a Graph Database engine into an SDN Controller. (B/M/P) [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat] [https://wiki.net.informatik.uni-goettingen.de/wiki/Gavel details]

* '''New!''' [[SDN Simulator: Implementation and validation of NS-3 or OMNET++ based SDN Simulator ]] (B/M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]

* '''New!''' [[Open SDN Testbed: Realize the SDN testbed and automation of network topologies using the EU GEANT Testbed services ]] (B/M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]

* '''New!''' A graph database tuning. (B/M/P) [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat] [https://wiki.net.informatik.uni-goettingen.de/wiki/Gavel details]

* '''New!''' Implementing more Gavel application by exploiting Graph algorithms. (B/M/P) [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat] [https://wiki.net.informatik.uni-goettingen.de/wiki/Gavel details]

* '''New!''' Demonstrating Security Vulnerabilities of SDN Controller (ONOS) (B/M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/abhinandan_s_prasad Abhinandan S Prasad]

* '''New!''' Modeling Performance of SDN topologies using Queuing theory (B/M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/abhinandan_s_prasad Abhinandan S Prasad]

* '''New!''' Implementation of sFlow for ONOS (Migrating existing code to new ONOS version (B/P) contact with [http://www.net.informatik.uni-goettingen.de/people/abhinandan_s_prasad Abhinandan S Prasad]

* '''New!''' Implementation of virtual switch using libfluid Openflow C++ library (B/P) contact with [http://www.net.informatik.uni-goettingen.de/people/abhinandan_s_prasad Abhinandan S Prasad]

===Network Function Virtualization (NFV) ===
* '''New!''' [[Management and Orchestration: Design and Implementation of NFV Management and Orchestration Layer with OpenStack, based on the ESTI NFVI-MANO and OPNFV frameworks.]] (M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]

* '''New!''' [[NSH Routing: Implementation of Network Service Headers to realize the service chain by steering traffic across the VNFs.]] (M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]

* '''New!''' [[VNF components: Implementation of Virtual Network Functions like Proxy Engines, Firewall, IDS and IPS, on top of OpenNetVM, Docker engines using the available open source tools. ]] (M/P) contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]

=== Future Internet architecture ===

* if you are interested in other topics in this area please get in contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]

* '''New!''' [[Video Delivery: Implementation and validation of SAID, a congestion control protocol for Multicast (A joint project with CISCO) ]] (B/M/P)

* '''New!''' [[IoT: Implementation of a Service using Named Function Networking for supporting essential functions of IoT]] (B/M/P)

* '''New!''' [[Interactive Video: Implementing a interactive video application in ICN using the NeMoI architecture]] (B/M/P)

* '''New!''' [[Gateway: Extend Named Function Networking to support protocol Translation]] (B/M/P)

* [[Network Management: Information Centric Networking (ICN) based solution for Network Management]] (B/M/P)

* '''New!''' [[Develop a web server for displaying statistics using restful service]] (B/M/P)

* '''New!''' [[A video resolver for android application]] (B/M/P)

* [[Infrastructureless, Delay Tolerant Network in different Context: Internet of Things, Emergency, Mobile Social Networks, Pervasive Computing]] (B/M/P) (currently unavailable)

* [[Disaster Recovery: Implementation and evaluation of Mobile phone based Information Centric Networking (ICN) solution for support during disasters]] (B/M/P) (currently unavailable)

* [[Wireless mesh networks/vehicular networks/wireless sensor networks: Information Centric Networking (ICN) based solution]] (B/M/P) (currently unavailable)

=== Data Crawling and analysis ===

* [[Large scale distributed Data crawling and analysis of a popular web service]] (B/M/P)

* '''New!''' [[Data crawling and analysis of Twitter]] (P) ([http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao])

* if you are interested in other topics in this area please get in contact with [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]

=== Massive Data Mining and Recommender System===

* [[Data Mining of the Web : User Behavior Analysis]] (B/M/P) [http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]

* [[Building the Genealogy for Researchers]] (B/M/P)[http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]

* [[Recommender System Design]] (B/M/P)[http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]

* if you are interested in other topics in this area please get in contact with [http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]

=== Social Networking ===
* '''New!''' [[Goettingen Assistant: Android App Development]] (P) ([http://www.net.informatik.uni-goettingen.de/people/shichang_ding Shichang Ding])
* [[Topic prediction in online social networks]] (B/M/P)([http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang])
* [[Mining emotion patterns in online social networks]] (B/M/P)([http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang])
* Mining human mobility pattern from intra-city traffic data (B/M/P) ([http://www.net.informatik.uni-goettingen.de/people/shichang_ding Shichang Ding])

=== Information Centric Networking (ICN) ===
* ICN over GTS: exploit Geant Testbed Service to build configurable ICN testbeds (B/M/P) ([http://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto])
* ICNProSe: ICN-based Proximity Discovery Services (B/M/P) ([http://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto])

== Ongoing Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Topic advisor'''
|{{Hl2}} |'''Initial readings'''
|{{Hl2}} |'''Description'''
|{{Hl2}} |'''Student'''

|-
| Design and Implementation of a distributed OSN on Home Gateways (Student project and Master's Thesis)
|[http://user.informatik.uni-goettingen.de/~dkoll David Koll]
|
|
| Assigned to Dieter Lechler
|-
|Sentiment Analysis (Student project)
|[http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]
|
|
| Assigned to Beatrice Kateule
|-
| Analysis of Business Transitions: A Case Study of Yelp (Bachelor Thesis)
|[http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]
|
|
| Assigned to Marcus Thomas Khalil
|-
| Understanding Group Patterns in Q&A Services (Bachelor Thesis)
|[http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]
|
|
| Assigned to Jonas Koopmann
|-
| COPSS-lite : Lightweight ICN Based Pub/Sub for IoT Environments (Master Thesis)
| [http://www.net.informatik.uni-goettingen.de/people/sripriya%20srikant_adhatarao Sripriya]
|
|
| Assigned to Haitao Wang
|-
| A ICN Gateway for IoT (Bachelor Thesis)
| [http://www.net.informatik.uni-goettingen.de/people/sripriya%20srikant_adhatarao Sripriya]
|
|
| Assigned to Janosch Ruff
|-
|}

== Completed Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Topic advisor'''
|{{Hl2}} |'''Initial readings'''
|{{Hl2}} |'''Description'''
|{{Hl2}} |'''Student'''

|-
| Personalized Recommender System Design (Master thesis Project)
| [http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang]
|
| Build a personalized context-aware recommender system for customers according to their own interest.
| Completed by Haile Misgna
|-
| Emotion Patterns Analysis in OSNs (Bachelor thesis Project)
| [http://www.net.informatik.uni-goettingen.de/people/hong_huang Hong Huang],[http://www.net.informatik.uni-goettingen.de/people/xu_chen Xu Chen]
|
| We aim to study the emotion patterns in the Twitter service and predict the future emotion status of users.
| Completed by Stefan Peters
|-
| Implementation of a pub/sub system (Student project)
| [http://www.net.informatik.uni-goettingen.de/people/jiachen_chen Jiachen Chen] [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]
|
| The aim of the work is to show how application layer intelligence cupled with network layer pub/sub can be beneficial to both users as well as network operators
| Completed by Sripriya
|-
| Large Scale Distributed Natural Language Document Generation System (Student project at IBM)
| [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]
|
| The work was done at IBM
| Completed by Eeran Maiti
|-
| Investigate real time streaming tools for large scale data processing (Student project)
| [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]
|
| The aim of the work is to compare real time streaming tools.
| Completed by Ram
|-
| Software-Defined Networking and Network Operating System (Student project)
| [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Mayutan Arumaithurai]
|
| SDN based ntwork operating system
| Completed by Rasha
|-
| GEMSTONE goes Mobile (BSc Thesis/Student Project)
| [http://user.informatik.uni-goettingen.de/~dkoll David Koll]
|
| Portation of a Decentralized Online Social Network to the Android Platform
| Completed by Fabien Mathey and improved by Eeran Maiti
|-
| Transitioning of Social Graphs between Multiple Online Social Networks (BSc Thesis)
| [http://user.informatik.uni-goettingen.de/~dkoll David Koll]
|
| Portation of friendship graphs between different Online Social Networks
| Completed by Kai-Stephan Jacobsen
|-
| Prevention and Mitigation of (D)DoS Attacks in Enterprise Environments (BSc Thesis)
| [http://user.informatik.uni-goettingen.de/~dkoll David Koll]
|
| An analysis of enterprise infrastructures and their vulnerarbility towards attacks from the outside.
| Completed by David Kelterer
|-
| Sybils in Disguise: An Attacker View on OSN-based Sybil Defenses (Student Project and MSc Thesis)
| [http://user.informatik.uni-goettingen.de/~dkoll David Koll]
|
| An analysis of fake detection approaches in social networks.
| Completed by Martin Schwarzmaier
|-
|}

* For a full list of older topics please go [http://www.net.informatik.uni-goettingen.de/student_projects here].
</noinclude>

Computer Networks (Winter 2017/2018)

2018-02-07T10:11:41Z

Dkoll: /* Details */

== Details ==
{{CourseDetails
|credits=120h, 4 ECTS (old PO), 5 ECTS (new PO)
|module=B.Inf.902: Telematik (old), B.Inf.1204.Telematik/Computernetzwerke (new)
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|ta= [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat, MSc.]
|time=Lecture: Thursday, 10am-12pm, Exercise: Thursday, 12pm-1pm
|place= [http://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&moduleCall=webInfo&publishConfFile=webInfoRaum&publishSubDir=raum&keep=y&raum.rgid=22 Mikrobiologie-Hörsaalgebäude - MN06] [http://univz.uni-goettingen.de/qisserver/rds?state=wsearchv&search=3&raum.dtxt=MN06&P_start=0&P_anzahl=10&_form=display# Google Maps]
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=183540&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung Link]
}}
{{Announcement|Note:
The Final exam started as announced before at 12 PM!!. }}
{{Announcement|Note:
Please be aware that the final exam will be hosted in MN08 - GZG}}

==Course description==
Principles and basic understanding of computer networking, with an emphasis on the Internet. Topics include: the concepts and components of computer networks, packet switching, layered architectures, TCP/IP, error control, window flow control, local area networks, network layer and mobility, transport layer and congestion control, Quality of Service and multimedia networking, network management and security, and an introduction to current research topics.
After this course students should have general knowledge on basic concepts of networking, how the Internet works and basic network programming.

==Preliminary Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''Date'''
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Slides'''
|{{Hl2}} |'''Excercises'''
|{{Hl2}} |'''Excercise notes'''
|-
| align="right" | 19.10.2017
| Introduction & Layering
| [[Media:CN_WS20172018_1.pdf | pdf]]
| [[Media:CN_WS20172018_ex1.pdf | pdf]]
| [[Media:CN_WS20172018_ex1_sol.pdf | pdf]]
|-
| align="right" | 26.10.2017
| Link Layer I
| [[Media:CN_WS20172018_2.pdf | pdf]]
| [[Media:CN_WS20172018_ex2.pdf | pdf]]
| [[Media:CN_WS20172018_ex2_sol_edited.pdf | pdf]]
|-
| align="right" | 02.11.2017
| Link Layer II
| [[Media:CN_WS20172018_3.pdf | pdf]]
| [[Media:CN_WS20172018_ex3.pdf | pdf]]
| [[Media:CN_WS20172018_ex3_sol.pdf | pdf]]
|-
| align="right" | 09.11.2017
| Network Layer I
| [[Media:CN_WS20172018_4.pdf | pdf]]
| [[Media:CN_WS20172018_ex4.pdf | pdf]]
|[[Media:CN_WS20172018_ex4_sol.pdf | pdf]]
|-
| align="right" | 16.11.2017
| Network Layer II
| [[Media:CN_WS20172018_5.pdf | pdf]]
| [[Media:CN_WS20172018_ex5.pdf | pdf]]
|[[Media:CN_WS20172018_ex5_sol.pdf | pdf]]
|-
| align="right" | 23.11.2017
| Network Layer III
| [[Media:CN_WS20172018_6_1.pdf | pdf]]
| [[Media:CN_WS20172018_ex6?.pdf | pdf]]
|[[Media:CN_WS20172018_ex6_sol.pdf | pdf]]
|-
| align="right" | 30.11.2017
| Transport Layer I
| [[Media:CN_WS20172018_7.pdf | pdf]]
| [[Media:CN_WS20172018_ex7.pdf | pdf]]
|[[Media:CN_WS20172018_ex7_sol.pdf | pdf]]
|-
| align="right" | 07.12.2017
| Transport Layer II
|[[Media:CN_WS20172018_8.pdf | pdf]]
| [[Media:CN_WS20172018_ex8.pdf | pdf]]
|[[Media:CN_WS20172018_ex8_sol.pdf | pdf]]
|-
| align="right" | 14.12.2017
| Networked Multimedia
|[[Media:CN_WS20172018_9.pdf | pdf]]
| [[Media:CN_WS20172018_ex9.pdf | pdf]]
|[[Media:CN_WS20172018_ex9_sol.pdf | pdf]]
|-
| align="right" | 21.12.2017
| Quality of Service
|[[Media:CN_WS20172018_10.pdf | pdf]]
| [[Media:CN_WS20172018_ex10.pdf | pdf]]
|[[Media:CN_WS20172018_ex10_sol.pdf | pdf]]
|-
| align="right" | 11.01.2018
| Network Security I
|[[Media:CN_WS20172018_11.pdf | pdf]]
| [[Media:CN_WS20172018_ex11.pdf | pdf]]
|[[Media:CN_WS20172018_ex11_sol.pdf | pdf]]
|-
| align="right" | 18.01.2018
| Network Security II
|[[Media:CN_WS20172018_12.pdf | pdf]]
| [[Media:CN_WS20172018_ex12.pdf | pdf]]
|[[Media:CN_WS20172018_ex12_sol_updated_fixedtheexamtime.pdf | pdf]]
|-
| align="right" | 25.01.2018
| Exercise 12 and Q&A session
|
|
|
|-
| align="right" | 07.02.2018
| Written Examination
| 12PM - 2PM
| [https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&moduleCall=webInfo&publishConfFile=webInfoRaum&publishSubDir=raum&keep=y&raum.rgid=193 MN-08]
|
|-
|}
{{Announcement|Note:
There will be a Q&A session. Thank you Helen Busch for your Questions!}}

==Old exam sheets==
The following old exam sheets are provided for ''self-study'' purposes. They will not be discussed during an exercise session, nor will we provide answer sheets. However, you can email questions regarding a particular exercise to the TA's to be considered during the Q&A session at the end of the semester.
* [[Media:ComputerNetworks_ws2009_exam.pdf | Exam sheet winter semester 2009]]
* [[Media:Telematik_WS2009_ee.pdf | Mock exam winter semester 2009]]
* [[Media:ComputerNetworks_SS2010_exam.pdf | Exam summer semester 2010]]

==Textbook==
* J. Kurose and K. Ross, [http://www.aw.com/info/kurose/about.html "Computer Networking: A Top-Down Approach Featuring the Internet"], 6th edition, Addison-Wesley, 2014.
* A. S. Tanenbaum, [http://authors.phptr.com/tanenbaumcn4/ "Computer Networks"], 4th edition, Prentice Hall, 2002.

==Additional References==
* W. Richard Stevens, [http://www.kohala.com/start/tcpipiv1.html "TCP/IP Illustrated, Volume 1: The Protocols"], Addison-Wesley, 1994.
* W. Richard Stevens, [http://www.kohala.com/start/unpv12e.html "UNIX Network Programming, Volume 1: Networking APIs"], 2nd edition, Prentice Hall, 1997.

== Other Resources==
* Movie: "Warriors of the Net" [http://www.warriorsofthe.net/]

==Prerequisites==
* Computer Science I, II; basic familiarity with UNIX and C.

[[Category:Courses]]

Computer Networks (Winter 2017/2018)

2018-02-07T10:11:13Z

Dkoll: /* Details */

== Details ==
{{CourseDetails
|credits=120h, 4 ECTS (old PO), 5 ECTS (new PO)
|module=B.Inf.902: Telematik (old), B.Inf.1204.Telematik/Computernetzwerke (new)
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|ta= [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat, MSc.]
|time=Lecture: Thursday, 10am-12pm, Exercise: Thursday, 12pm-1pm
|place= [http://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&moduleCall=webInfo&publishConfFile=webInfoRaum&publishSubDir=raum&keep=y&raum.rgid=22 Mikrobiologie-Hörsaalgebäude - MN06] [http://univz.uni-goettingen.de/qisserver/rds?state=wsearchv&search=3&raum.dtxt=MN06&P_start=0&P_anzahl=10&_form=display# Google Maps]
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=183540&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung Link]
}}
{{Announcement|Note:
The Final exam started as announced before at 12 PM!!. }}
{{Announcement|Note:
Please be aware that the final exam will be hosted in MN08 - GZG - [https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&moduleCall=webInfo&publishConfFile=webInfoRaum&publishSubDir=raum&keep=y&raum.rgid=193 Link]}}

==Course description==
Principles and basic understanding of computer networking, with an emphasis on the Internet. Topics include: the concepts and components of computer networks, packet switching, layered architectures, TCP/IP, error control, window flow control, local area networks, network layer and mobility, transport layer and congestion control, Quality of Service and multimedia networking, network management and security, and an introduction to current research topics.
After this course students should have general knowledge on basic concepts of networking, how the Internet works and basic network programming.

==Preliminary Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''Date'''
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Slides'''
|{{Hl2}} |'''Excercises'''
|{{Hl2}} |'''Excercise notes'''
|-
| align="right" | 19.10.2017
| Introduction & Layering
| [[Media:CN_WS20172018_1.pdf | pdf]]
| [[Media:CN_WS20172018_ex1.pdf | pdf]]
| [[Media:CN_WS20172018_ex1_sol.pdf | pdf]]
|-
| align="right" | 26.10.2017
| Link Layer I
| [[Media:CN_WS20172018_2.pdf | pdf]]
| [[Media:CN_WS20172018_ex2.pdf | pdf]]
| [[Media:CN_WS20172018_ex2_sol_edited.pdf | pdf]]
|-
| align="right" | 02.11.2017
| Link Layer II
| [[Media:CN_WS20172018_3.pdf | pdf]]
| [[Media:CN_WS20172018_ex3.pdf | pdf]]
| [[Media:CN_WS20172018_ex3_sol.pdf | pdf]]
|-
| align="right" | 09.11.2017
| Network Layer I
| [[Media:CN_WS20172018_4.pdf | pdf]]
| [[Media:CN_WS20172018_ex4.pdf | pdf]]
|[[Media:CN_WS20172018_ex4_sol.pdf | pdf]]
|-
| align="right" | 16.11.2017
| Network Layer II
| [[Media:CN_WS20172018_5.pdf | pdf]]
| [[Media:CN_WS20172018_ex5.pdf | pdf]]
|[[Media:CN_WS20172018_ex5_sol.pdf | pdf]]
|-
| align="right" | 23.11.2017
| Network Layer III
| [[Media:CN_WS20172018_6_1.pdf | pdf]]
| [[Media:CN_WS20172018_ex6?.pdf | pdf]]
|[[Media:CN_WS20172018_ex6_sol.pdf | pdf]]
|-
| align="right" | 30.11.2017
| Transport Layer I
| [[Media:CN_WS20172018_7.pdf | pdf]]
| [[Media:CN_WS20172018_ex7.pdf | pdf]]
|[[Media:CN_WS20172018_ex7_sol.pdf | pdf]]
|-
| align="right" | 07.12.2017
| Transport Layer II
|[[Media:CN_WS20172018_8.pdf | pdf]]
| [[Media:CN_WS20172018_ex8.pdf | pdf]]
|[[Media:CN_WS20172018_ex8_sol.pdf | pdf]]
|-
| align="right" | 14.12.2017
| Networked Multimedia
|[[Media:CN_WS20172018_9.pdf | pdf]]
| [[Media:CN_WS20172018_ex9.pdf | pdf]]
|[[Media:CN_WS20172018_ex9_sol.pdf | pdf]]
|-
| align="right" | 21.12.2017
| Quality of Service
|[[Media:CN_WS20172018_10.pdf | pdf]]
| [[Media:CN_WS20172018_ex10.pdf | pdf]]
|[[Media:CN_WS20172018_ex10_sol.pdf | pdf]]
|-
| align="right" | 11.01.2018
| Network Security I
|[[Media:CN_WS20172018_11.pdf | pdf]]
| [[Media:CN_WS20172018_ex11.pdf | pdf]]
|[[Media:CN_WS20172018_ex11_sol.pdf | pdf]]
|-
| align="right" | 18.01.2018
| Network Security II
|[[Media:CN_WS20172018_12.pdf | pdf]]
| [[Media:CN_WS20172018_ex12.pdf | pdf]]
|[[Media:CN_WS20172018_ex12_sol_updated_fixedtheexamtime.pdf | pdf]]
|-
| align="right" | 25.01.2018
| Exercise 12 and Q&A session
|
|
|
|-
| align="right" | 07.02.2018
| Written Examination
| 12PM - 2PM
| [https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&moduleCall=webInfo&publishConfFile=webInfoRaum&publishSubDir=raum&keep=y&raum.rgid=193 MN-08]
|
|-
|}
{{Announcement|Note:
There will be a Q&A session. Thank you Helen Busch for your Questions!}}

==Old exam sheets==
The following old exam sheets are provided for ''self-study'' purposes. They will not be discussed during an exercise session, nor will we provide answer sheets. However, you can email questions regarding a particular exercise to the TA's to be considered during the Q&A session at the end of the semester.
* [[Media:ComputerNetworks_ws2009_exam.pdf | Exam sheet winter semester 2009]]
* [[Media:Telematik_WS2009_ee.pdf | Mock exam winter semester 2009]]
* [[Media:ComputerNetworks_SS2010_exam.pdf | Exam summer semester 2010]]

==Textbook==
* J. Kurose and K. Ross, [http://www.aw.com/info/kurose/about.html "Computer Networking: A Top-Down Approach Featuring the Internet"], 6th edition, Addison-Wesley, 2014.
* A. S. Tanenbaum, [http://authors.phptr.com/tanenbaumcn4/ "Computer Networks"], 4th edition, Prentice Hall, 2002.

==Additional References==
* W. Richard Stevens, [http://www.kohala.com/start/tcpipiv1.html "TCP/IP Illustrated, Volume 1: The Protocols"], Addison-Wesley, 1994.
* W. Richard Stevens, [http://www.kohala.com/start/unpv12e.html "UNIX Network Programming, Volume 1: Networking APIs"], 2nd edition, Prentice Hall, 1997.

== Other Resources==
* Movie: "Warriors of the Net" [http://www.warriorsofthe.net/]

==Prerequisites==
* Computer Science I, II; basic familiarity with UNIX and C.

[[Category:Courses]]

Computer Networks (Winter 2017/2018)

2018-02-07T10:10:34Z

Dkoll: /* Details */

== Details ==
{{CourseDetails
|credits=120h, 4 ECTS (old PO), 5 ECTS (new PO)
|module=B.Inf.902: Telematik (old), B.Inf.1204.Telematik/Computernetzwerke (new)
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|ta= [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat, MSc.]
|time=Lecture: Thursday, 10am-12pm, Exercise: Thursday, 12pm-1pm
|place= [http://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&moduleCall=webInfo&publishConfFile=webInfoRaum&publishSubDir=raum&keep=y&raum.rgid=22 Mikrobiologie-Hörsaalgebäude - MN06] [http://univz.uni-goettingen.de/qisserver/rds?state=wsearchv&search=3&raum.dtxt=MN06&P_start=0&P_anzahl=10&_form=display# Google Maps]
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=183540&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung Link]
}}
{{Announcement|Note:
The Final exam started as announced before at 12 PM!!. }}
{{Announcement|Note:
Please be aware that the final exam will be hosted in MN08 - GZG. [https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&moduleCall=webInfo&publishConfFile=webInfoRaum&publishSubDir=raum&keep=y&raum.rgid=193]}}

==Course description==
Principles and basic understanding of computer networking, with an emphasis on the Internet. Topics include: the concepts and components of computer networks, packet switching, layered architectures, TCP/IP, error control, window flow control, local area networks, network layer and mobility, transport layer and congestion control, Quality of Service and multimedia networking, network management and security, and an introduction to current research topics.
After this course students should have general knowledge on basic concepts of networking, how the Internet works and basic network programming.

==Preliminary Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''Date'''
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Slides'''
|{{Hl2}} |'''Excercises'''
|{{Hl2}} |'''Excercise notes'''
|-
| align="right" | 19.10.2017
| Introduction & Layering
| [[Media:CN_WS20172018_1.pdf | pdf]]
| [[Media:CN_WS20172018_ex1.pdf | pdf]]
| [[Media:CN_WS20172018_ex1_sol.pdf | pdf]]
|-
| align="right" | 26.10.2017
| Link Layer I
| [[Media:CN_WS20172018_2.pdf | pdf]]
| [[Media:CN_WS20172018_ex2.pdf | pdf]]
| [[Media:CN_WS20172018_ex2_sol_edited.pdf | pdf]]
|-
| align="right" | 02.11.2017
| Link Layer II
| [[Media:CN_WS20172018_3.pdf | pdf]]
| [[Media:CN_WS20172018_ex3.pdf | pdf]]
| [[Media:CN_WS20172018_ex3_sol.pdf | pdf]]
|-
| align="right" | 09.11.2017
| Network Layer I
| [[Media:CN_WS20172018_4.pdf | pdf]]
| [[Media:CN_WS20172018_ex4.pdf | pdf]]
|[[Media:CN_WS20172018_ex4_sol.pdf | pdf]]
|-
| align="right" | 16.11.2017
| Network Layer II
| [[Media:CN_WS20172018_5.pdf | pdf]]
| [[Media:CN_WS20172018_ex5.pdf | pdf]]
|[[Media:CN_WS20172018_ex5_sol.pdf | pdf]]
|-
| align="right" | 23.11.2017
| Network Layer III
| [[Media:CN_WS20172018_6_1.pdf | pdf]]
| [[Media:CN_WS20172018_ex6?.pdf | pdf]]
|[[Media:CN_WS20172018_ex6_sol.pdf | pdf]]
|-
| align="right" | 30.11.2017
| Transport Layer I
| [[Media:CN_WS20172018_7.pdf | pdf]]
| [[Media:CN_WS20172018_ex7.pdf | pdf]]
|[[Media:CN_WS20172018_ex7_sol.pdf | pdf]]
|-
| align="right" | 07.12.2017
| Transport Layer II
|[[Media:CN_WS20172018_8.pdf | pdf]]
| [[Media:CN_WS20172018_ex8.pdf | pdf]]
|[[Media:CN_WS20172018_ex8_sol.pdf | pdf]]
|-
| align="right" | 14.12.2017
| Networked Multimedia
|[[Media:CN_WS20172018_9.pdf | pdf]]
| [[Media:CN_WS20172018_ex9.pdf | pdf]]
|[[Media:CN_WS20172018_ex9_sol.pdf | pdf]]
|-
| align="right" | 21.12.2017
| Quality of Service
|[[Media:CN_WS20172018_10.pdf | pdf]]
| [[Media:CN_WS20172018_ex10.pdf | pdf]]
|[[Media:CN_WS20172018_ex10_sol.pdf | pdf]]
|-
| align="right" | 11.01.2018
| Network Security I
|[[Media:CN_WS20172018_11.pdf | pdf]]
| [[Media:CN_WS20172018_ex11.pdf | pdf]]
|[[Media:CN_WS20172018_ex11_sol.pdf | pdf]]
|-
| align="right" | 18.01.2018
| Network Security II
|[[Media:CN_WS20172018_12.pdf | pdf]]
| [[Media:CN_WS20172018_ex12.pdf | pdf]]
|[[Media:CN_WS20172018_ex12_sol_updated_fixedtheexamtime.pdf | pdf]]
|-
| align="right" | 25.01.2018
| Exercise 12 and Q&A session
|
|
|
|-
| align="right" | 07.02.2018
| Written Examination
| 12PM - 2PM
| [https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&moduleCall=webInfo&publishConfFile=webInfoRaum&publishSubDir=raum&keep=y&raum.rgid=193 MN-08]
|
|-
|}
{{Announcement|Note:
There will be a Q&A session. Thank you Helen Busch for your Questions!}}

==Old exam sheets==
The following old exam sheets are provided for ''self-study'' purposes. They will not be discussed during an exercise session, nor will we provide answer sheets. However, you can email questions regarding a particular exercise to the TA's to be considered during the Q&A session at the end of the semester.
* [[Media:ComputerNetworks_ws2009_exam.pdf | Exam sheet winter semester 2009]]
* [[Media:Telematik_WS2009_ee.pdf | Mock exam winter semester 2009]]
* [[Media:ComputerNetworks_SS2010_exam.pdf | Exam summer semester 2010]]

==Textbook==
* J. Kurose and K. Ross, [http://www.aw.com/info/kurose/about.html "Computer Networking: A Top-Down Approach Featuring the Internet"], 6th edition, Addison-Wesley, 2014.
* A. S. Tanenbaum, [http://authors.phptr.com/tanenbaumcn4/ "Computer Networks"], 4th edition, Prentice Hall, 2002.

==Additional References==
* W. Richard Stevens, [http://www.kohala.com/start/tcpipiv1.html "TCP/IP Illustrated, Volume 1: The Protocols"], Addison-Wesley, 1994.
* W. Richard Stevens, [http://www.kohala.com/start/unpv12e.html "UNIX Network Programming, Volume 1: Networking APIs"], 2nd edition, Prentice Hall, 1997.

== Other Resources==
* Movie: "Warriors of the Net" [http://www.warriorsofthe.net/]

==Prerequisites==
* Computer Science I, II; basic familiarity with UNIX and C.

[[Category:Courses]]

Computer Networks (Winter 2017/2018)

2018-02-07T10:10:17Z

Dkoll: /* Details */

== Details ==
{{CourseDetails
|credits=120h, 4 ECTS (old PO), 5 ECTS (new PO)
|module=B.Inf.902: Telematik (old), B.Inf.1204.Telematik/Computernetzwerke (new)
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|ta= [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat, MSc.]
|time=Lecture: Thursday, 10am-12pm, Exercise: Thursday, 12pm-1pm
|place= [http://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&moduleCall=webInfo&publishConfFile=webInfoRaum&publishSubDir=raum&keep=y&raum.rgid=22 Mikrobiologie-Hörsaalgebäude - MN06] [http://univz.uni-goettingen.de/qisserver/rds?state=wsearchv&search=3&raum.dtxt=MN06&P_start=0&P_anzahl=10&_form=display# Google Maps]
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=183540&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung Link]
}}
{{Announcement|Note:
The Final exam started as announced before at 12 PM!!. }}
{{Announcement|Note:
Please be aware that the final exam will be hosted in MN08 - GZG. https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&moduleCall=webInfo&publishConfFile=webInfoRaum&publishSubDir=raum&keep=y&raum.rgid=193}}

==Course description==
Principles and basic understanding of computer networking, with an emphasis on the Internet. Topics include: the concepts and components of computer networks, packet switching, layered architectures, TCP/IP, error control, window flow control, local area networks, network layer and mobility, transport layer and congestion control, Quality of Service and multimedia networking, network management and security, and an introduction to current research topics.
After this course students should have general knowledge on basic concepts of networking, how the Internet works and basic network programming.

==Preliminary Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''Date'''
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Slides'''
|{{Hl2}} |'''Excercises'''
|{{Hl2}} |'''Excercise notes'''
|-
| align="right" | 19.10.2017
| Introduction & Layering
| [[Media:CN_WS20172018_1.pdf | pdf]]
| [[Media:CN_WS20172018_ex1.pdf | pdf]]
| [[Media:CN_WS20172018_ex1_sol.pdf | pdf]]
|-
| align="right" | 26.10.2017
| Link Layer I
| [[Media:CN_WS20172018_2.pdf | pdf]]
| [[Media:CN_WS20172018_ex2.pdf | pdf]]
| [[Media:CN_WS20172018_ex2_sol_edited.pdf | pdf]]
|-
| align="right" | 02.11.2017
| Link Layer II
| [[Media:CN_WS20172018_3.pdf | pdf]]
| [[Media:CN_WS20172018_ex3.pdf | pdf]]
| [[Media:CN_WS20172018_ex3_sol.pdf | pdf]]
|-
| align="right" | 09.11.2017
| Network Layer I
| [[Media:CN_WS20172018_4.pdf | pdf]]
| [[Media:CN_WS20172018_ex4.pdf | pdf]]
|[[Media:CN_WS20172018_ex4_sol.pdf | pdf]]
|-
| align="right" | 16.11.2017
| Network Layer II
| [[Media:CN_WS20172018_5.pdf | pdf]]
| [[Media:CN_WS20172018_ex5.pdf | pdf]]
|[[Media:CN_WS20172018_ex5_sol.pdf | pdf]]
|-
| align="right" | 23.11.2017
| Network Layer III
| [[Media:CN_WS20172018_6_1.pdf | pdf]]
| [[Media:CN_WS20172018_ex6?.pdf | pdf]]
|[[Media:CN_WS20172018_ex6_sol.pdf | pdf]]
|-
| align="right" | 30.11.2017
| Transport Layer I
| [[Media:CN_WS20172018_7.pdf | pdf]]
| [[Media:CN_WS20172018_ex7.pdf | pdf]]
|[[Media:CN_WS20172018_ex7_sol.pdf | pdf]]
|-
| align="right" | 07.12.2017
| Transport Layer II
|[[Media:CN_WS20172018_8.pdf | pdf]]
| [[Media:CN_WS20172018_ex8.pdf | pdf]]
|[[Media:CN_WS20172018_ex8_sol.pdf | pdf]]
|-
| align="right" | 14.12.2017
| Networked Multimedia
|[[Media:CN_WS20172018_9.pdf | pdf]]
| [[Media:CN_WS20172018_ex9.pdf | pdf]]
|[[Media:CN_WS20172018_ex9_sol.pdf | pdf]]
|-
| align="right" | 21.12.2017
| Quality of Service
|[[Media:CN_WS20172018_10.pdf | pdf]]
| [[Media:CN_WS20172018_ex10.pdf | pdf]]
|[[Media:CN_WS20172018_ex10_sol.pdf | pdf]]
|-
| align="right" | 11.01.2018
| Network Security I
|[[Media:CN_WS20172018_11.pdf | pdf]]
| [[Media:CN_WS20172018_ex11.pdf | pdf]]
|[[Media:CN_WS20172018_ex11_sol.pdf | pdf]]
|-
| align="right" | 18.01.2018
| Network Security II
|[[Media:CN_WS20172018_12.pdf | pdf]]
| [[Media:CN_WS20172018_ex12.pdf | pdf]]
|[[Media:CN_WS20172018_ex12_sol_updated_fixedtheexamtime.pdf | pdf]]
|-
| align="right" | 25.01.2018
| Exercise 12 and Q&A session
|
|
|
|-
| align="right" | 07.02.2018
| Written Examination
| 12PM - 2PM
| [https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&moduleCall=webInfo&publishConfFile=webInfoRaum&publishSubDir=raum&keep=y&raum.rgid=193 MN-08]
|
|-
|}
{{Announcement|Note:
There will be a Q&A session. Thank you Helen Busch for your Questions!}}

==Old exam sheets==
The following old exam sheets are provided for ''self-study'' purposes. They will not be discussed during an exercise session, nor will we provide answer sheets. However, you can email questions regarding a particular exercise to the TA's to be considered during the Q&A session at the end of the semester.
* [[Media:ComputerNetworks_ws2009_exam.pdf | Exam sheet winter semester 2009]]
* [[Media:Telematik_WS2009_ee.pdf | Mock exam winter semester 2009]]
* [[Media:ComputerNetworks_SS2010_exam.pdf | Exam summer semester 2010]]

==Textbook==
* J. Kurose and K. Ross, [http://www.aw.com/info/kurose/about.html "Computer Networking: A Top-Down Approach Featuring the Internet"], 6th edition, Addison-Wesley, 2014.
* A. S. Tanenbaum, [http://authors.phptr.com/tanenbaumcn4/ "Computer Networks"], 4th edition, Prentice Hall, 2002.

==Additional References==
* W. Richard Stevens, [http://www.kohala.com/start/tcpipiv1.html "TCP/IP Illustrated, Volume 1: The Protocols"], Addison-Wesley, 1994.
* W. Richard Stevens, [http://www.kohala.com/start/unpv12e.html "UNIX Network Programming, Volume 1: Networking APIs"], 2nd edition, Prentice Hall, 1997.

== Other Resources==
* Movie: "Warriors of the Net" [http://www.warriorsofthe.net/]

==Prerequisites==
* Computer Science I, II; basic familiarity with UNIX and C.

[[Category:Courses]]

Advanced Practical Course Data Science for Computer Networks (Winter 2017/2018)

2017-12-05T10:31:55Z

Dkoll:

{{Announcement|Note: The room for this course has changed to the bigger room 0.101!)}}

{{Announcement|Note: The primary platform for communication in this course will be StudIP. All materials will be uploaded there.}}

== Details ==
{{CourseDetails
|credits=180h, 6 ECTS
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/david_koll Dr. David Koll]
|ta=None
|time=Thursday, 14-16
|place=Ifi 0.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=203182&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Organization==
In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets.

While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:

* Introduction to the practical machine learning pipeline
* Exploratory data analysis
* The Python Data Science stack
* How to deal with unbalanced data
* Advanced algorithms for Data Science (an overview of competition winning algorithms)
* Parameter tuning for predictive models

Students need to submit their solutions to tasks by specific deadlines throughout the course. Note that this course thus requires a continous effort throughout the whole semester.
Solutions for each task have to be presented in class. A final report needs to be submitted at the end of the semester (March 31).

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Coursera Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.).
*Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries

==Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''When?'''
|{{Hl2}} |'''What?'''
|-
| align="right" | 19.10.2017
| Lecture 1: Introduction & The Data Science Pipeline - Task 1: Release
|-
| align="right" | 26.10.2017
| Lecture 2: The Python Data Science Stack
|-
| align="right" | 02.11.2017
| Task 1: Intermediate meeting
|-
| align="right" | 09.11.2017
| No lecture
|-
| align="right" | 16.11.2017
| Task 1: Presentation of Exemplary Solution // Task 2: Release
|-
| align="right" | 23.11.2017
| Lecture 3: Advanced Algorithms for Data Science
|-
| align="right" | 30.11.2017
| Lecture 4: Evaluation and Tuning of Models
|-
| align="right" | 07.12.2017
| Task 2: Intermediate meeting
|-
| align="right" | 14.12.2017
| No lecture
|-
| align="right" | 21.12.2017
| Task 2: Presentations // Task 3: Release
|-
| align="right" | 04.01.2018
| No lecture
|-
| align="right" | 11.01.2018
| Task 3: Intermediate meeting I
|-
| align="right" | 18.01.2018
| No lecture
|-
| align="right" | 25.01.2018
| Task 3: Intermediate meeting II
|-
| align="right" | 01.02.2018
| No lecture
|-
| align="right" | 08.02.2018-22.02.2018
| Task 3: Presentations
|-
| align="right" | 31.03.2018
| Final Report deadline
|-
|}

File:DS 1718 L3.pdf

2017-11-23T16:34:48Z

Dkoll:

Advanced Practical Course Data Science for Computer Networks (Winter 2017/2018)

2017-11-23T16:34:38Z

Dkoll: /* Schedule */

{{Announcement|Note: The room for this course has changed to the bigger room 0.101!)}}

{{Announcement|Note: The primary platform for communication in this course will be StudIP. Please register for the course there.)}}

== Details ==
{{CourseDetails
|credits=180h, 6 ECTS
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/david_koll Dr. David Koll]
|ta=None
|time=Thursday, 14-16
|place=Ifi 0.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=203182&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Organization==
In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets.

While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:

* Introduction to the practical machine learning pipeline
* Exploratory data analysis
* The Python Data Science stack
* How to deal with unbalanced data
* Advanced algorithms for Data Science (an overview of competition winning algorithms)
* Parameter tuning for predictive models

Students need to submit their solutions to tasks by specific deadlines throughout the course. Note that this course thus requires a continous effort throughout the whole semester.
Solutions for each task have to be presented in class. A final report needs to be submitted at the end of the semester (March 31).

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Coursera Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.).
*Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries

==Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''When?'''
|{{Hl2}} |'''What?'''
|{{Hl2}} |'''Materials'''
|-
| align="right" | 19.10.2017
| Lecture 1: Introduction & The Data Science Pipeline - Task 1: Release
| [[Media:DS_1718_L1.pdf | Lecture Slides]] -- [https://user.informatik.uni-goettingen.de/~dkoll/courses/ds_pract/bike_sharing_ipynb.html Bike Sharing IPYNB] -- [https://www.kaggle.com/c/m-inf-1800-ws-17-18-task-1 Kaggle inClass Competition for Task 1]
|-
| align="right" | 26.10.2017
| Lecture 2: The Python Data Science Stack
| [[Media:DS_1718_L2.pdf | Lecture Slides]]
|-
| align="right" | 02.11.2017
| Task 1: Intermediate meeting
|
|-
| align="right" | 09.11.2017
| No lecture
|
|-
| align="right" | 16.11.2017
| Task 1: Presentation of Exemplary Solution // Task 2: Release
|
|-
| align="right" | 23.11.2017
| Lecture 3: Advanced Algorithms for Data Science
| [[Media:DS_1718_L3.pdf | Lecture Slides]]
|-
| align="right" | 30.11.2017
| Lecture 4: Evaluation and Tuning of Models
|
|-
| align="right" | 07.12.2017
| Task 2: Intermediate meeting
|
|-
| align="right" | 14.12.2017
| No lecture
|
|-
| align="right" | 21.12.2017
| Task 2: Presentations // Task 3: Release
|
|-
| align="right" | 04.01.2018
| No lecture
|
|-
| align="right" | 11.01.2018
| Task 3: Intermediate meeting I
|
|-
| align="right" | 18.01.2018
| No lecture
|
|-
| align="right" | 25.01.2018
| Task 3: Intermediate meeting II
|
|-
| align="right" | 01.02.2018
| No lecture
|
|-
| align="right" | 08.02.2018-22.02.2018
| Task 3: Presentations
|
|-
| align="right" | 31.03.2018
| Final Report deadline
|
|-
|}

Advanced Practical Course Data Science for Computer Networks (Winter 2017/2018)

2017-11-23T16:34:25Z

Dkoll: /* Schedule */

{{Announcement|Note: The room for this course has changed to the bigger room 0.101!)}}

{{Announcement|Note: The primary platform for communication in this course will be StudIP. Please register for the course there.)}}

== Details ==
{{CourseDetails
|credits=180h, 6 ECTS
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/david_koll Dr. David Koll]
|ta=None
|time=Thursday, 14-16
|place=Ifi 0.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=203182&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Organization==
In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets.

While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:

* Introduction to the practical machine learning pipeline
* Exploratory data analysis
* The Python Data Science stack
* How to deal with unbalanced data
* Advanced algorithms for Data Science (an overview of competition winning algorithms)
* Parameter tuning for predictive models

Students need to submit their solutions to tasks by specific deadlines throughout the course. Note that this course thus requires a continous effort throughout the whole semester.
Solutions for each task have to be presented in class. A final report needs to be submitted at the end of the semester (March 31).

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Coursera Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.).
*Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries

==Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''When?'''
|{{Hl2}} |'''What?'''
|{{Hl2}} |'''Materials'''
|-
| align="right" | 19.10.2017
| Lecture 1: Introduction & The Data Science Pipeline - Task 1: Release
| [[Media:DS_1718_L1.pdf | Lecture Slides]] -- [https://user.informatik.uni-goettingen.de/~dkoll/courses/ds_pract/bike_sharing_ipynb.html Bike Sharing IPYNB] -- [https://www.kaggle.com/c/m-inf-1800-ws-17-18-task-1 Kaggle inClass Competition for Task 1]
|-
| align="right" | 26.10.2017
| Lecture 2: The Python Data Science Stack
| [[Media:DS_1718_L2.pdf | Lecture Slides]]
|-
| align="right" | 02.11.2017
| Task 1: Intermediate meeting
|
|-
| align="right" | 09.11.2017
| No lecture
|
|-
| align="right" | 16.11.2017
| Task 1: Presentation of Exemplary Solution // Task 2: Release
|
|-
| align="right" | 23.11.2017
| Lecture 3: Advanced Algorithms for Data Science
| [[Media:DS_1718_L2.pdf | Lecture Slides]]
|-
| align="right" | 30.11.2017
| Lecture 4: Evaluation and Tuning of Models
|
|-
| align="right" | 07.12.2017
| Task 2: Intermediate meeting
|
|-
| align="right" | 14.12.2017
| No lecture
|
|-
| align="right" | 21.12.2017
| Task 2: Presentations // Task 3: Release
|
|-
| align="right" | 04.01.2018
| No lecture
|
|-
| align="right" | 11.01.2018
| Task 3: Intermediate meeting I
|
|-
| align="right" | 18.01.2018
| No lecture
|
|-
| align="right" | 25.01.2018
| Task 3: Intermediate meeting II
|
|-
| align="right" | 01.02.2018
| No lecture
|
|-
| align="right" | 08.02.2018-22.02.2018
| Task 3: Presentations
|
|-
| align="right" | 31.03.2018
| Final Report deadline
|
|-
|}

Advanced Practical Course Data Science for Computer Networks (Winter 2017/2018)

2017-11-13T13:28:02Z

Dkoll: /* Schedule */

{{Announcement|Note: The room for this course has changed to the bigger room 0.101!)}}

{{Announcement|Note: The primary platform for communication in this course will be StudIP. Please register for the course there.)}}

== Details ==
{{CourseDetails
|credits=180h, 6 ECTS
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/david_koll Dr. David Koll]
|ta=None
|time=Thursday, 14-16
|place=Ifi 0.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=203182&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Organization==
In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets.

While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:

* Introduction to the practical machine learning pipeline
* Exploratory data analysis
* The Python Data Science stack
* How to deal with unbalanced data
* Advanced algorithms for Data Science (an overview of competition winning algorithms)
* Parameter tuning for predictive models

Students need to submit their solutions to tasks by specific deadlines throughout the course. Note that this course thus requires a continous effort throughout the whole semester.
Solutions for each task have to be presented in class. A final report needs to be submitted at the end of the semester (March 31).

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Coursera Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.).
*Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries

==Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''When?'''
|{{Hl2}} |'''What?'''
|{{Hl2}} |'''Materials'''
|-
| align="right" | 19.10.2017
| Lecture 1: Introduction & The Data Science Pipeline - Task 1: Release
| [[Media:DS_1718_L1.pdf | Lecture Slides]] -- [https://user.informatik.uni-goettingen.de/~dkoll/courses/ds_pract/bike_sharing_ipynb.html Bike Sharing IPYNB] -- [https://www.kaggle.com/c/m-inf-1800-ws-17-18-task-1 Kaggle inClass Competition for Task 1]
|-
| align="right" | 26.10.2017
| Lecture 2: The Python Data Science Stack
| [[Media:DS_1718_L2.pdf | Lecture Slides]]
|-
| align="right" | 02.11.2017
| Task 1: Intermediate meeting
|
|-
| align="right" | 09.11.2017
| No lecture
|
|-
| align="right" | 16.11.2017
| Task 1: Presentation of Exemplary Solution // Task 2: Release
|
|-
| align="right" | 23.11.2017
| Lecture 3: Advanced Algorithms for Data Science
|
|-
| align="right" | 30.11.2017
| Lecture 4: Evaluation and Tuning of Models
|
|-
| align="right" | 07.12.2017
| Task 2: Intermediate meeting
|
|-
| align="right" | 14.12.2017
| No lecture
|
|-
| align="right" | 21.12.2017
| Task 2: Presentations // Task 3: Release
|
|-
| align="right" | 04.01.2018
| No lecture
|
|-
| align="right" | 11.01.2018
| Task 3: Intermediate meeting I
|
|-
| align="right" | 18.01.2018
| No lecture
|
|-
| align="right" | 25.01.2018
| Task 3: Intermediate meeting II
|
|-
| align="right" | 01.02.2018
| No lecture
|
|-
| align="right" | 08.02.2018-22.02.2018
| Task 3: Presentations
|
|-
| align="right" | 31.03.2018
| Final Report deadline
|
|-
|}

File:DS 1718 L2.pdf

2017-10-26T14:22:00Z

Dkoll:

Advanced Practical Course Data Science for Computer Networks (Winter 2017/2018)

2017-10-26T14:21:49Z

Dkoll:

{{Announcement|Note: The room for this course has changed to the bigger room 0.101!)}}

{{Announcement|Note: The primary platform for communication in this course will be StudIP. Please register for the course there.)}}

== Details ==
{{CourseDetails
|credits=180h, 6 ECTS
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/david_koll Dr. David Koll]
|ta=None
|time=Thursday, 14-16
|place=Ifi 0.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=203182&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Organization==
In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets.

While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:

* Introduction to the practical machine learning pipeline
* Exploratory data analysis
* The Python Data Science stack
* How to deal with unbalanced data
* Advanced algorithms for Data Science (an overview of competition winning algorithms)
* Parameter tuning for predictive models

Students need to submit their solutions to tasks by specific deadlines throughout the course. Note that this course thus requires a continous effort throughout the whole semester.
Solutions for each task have to be presented in class. A final report needs to be submitted at the end of the semester (March 31).

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Coursera Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.).
*Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries

==Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''When?'''
|{{Hl2}} |'''What?'''
|{{Hl2}} |'''Materials'''
|-
| align="right" | 19.10.2017
| Lecture 1: Introduction & The Data Science Pipeline - Task 1: Release
| [[Media:DS_1718_L1.pdf | Lecture Slides]] -- [https://user.informatik.uni-goettingen.de/~dkoll/courses/ds_pract/bike_sharing_ipynb.html Bike Sharing IPYNB] -- [https://www.kaggle.com/c/m-inf-1800-ws-17-18-task-1 Kaggle inClass Competition for Task 1]
|-
| align="right" | 26.10.2017
| Lecture 2: The Python Data Science Stack
| [[Media:DS_1718_L2.pdf | Lecture Slides]]
|-
| align="right" | 02.11.2017
| Task 1: Intermediate meeting
|
|-
| align="right" | 09.11.2017
| No lecture
|
|-
| align="right" | 16.11.2017
| Task 1: Presentations // Task 2: Release
|
|-
| align="right" | 23.11.2017
| Lecture 3: Advanced Algorithms for Data Science
|
|-
| align="right" | 30.11.2017
| Lecture 4: Evaluation and Tuning of Models
|
|-
| align="right" | 07.12.2017
| Task 2: Intermediate meeting
|
|-
| align="right" | 14.12.2017
| No lecture
|
|-
| align="right" | 21.12.2017
| Task 2: Presentations // Task 3: Release
|
|-
| align="right" | 04.01.2018
| No lecture
|
|-
| align="right" | 11.01.2018
| Task 3: Intermediate meeting I
|
|-
| align="right" | 18.01.2018
| No lecture
|
|-
| align="right" | 25.01.2018
| Task 3: Intermediate meeting II
|
|-
| align="right" | 01.02.2018
| No lecture
|
|-
| align="right" | 08.02.2018-22.02.2018
| Task 3: Presentations
|
|-
| align="right" | 31.03.2018
| Final Report deadline
|
|-
|}

Advanced Practical Course Data Science for Computer Networks (Winter 2017/2018)

2017-10-26T14:11:57Z

Dkoll:

{{Announcement|Note: The room for this course has changed to the bigger room 0.101!)}}

{{Announcement|Note: The primary platform for communication in this course will be StudIP. Please register for the course there.)}}

== Details ==
{{CourseDetails
|credits=180h, 6 ECTS
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/david_koll Dr. David Koll]
|ta=None
|time=Thursday, 14-16
|place=Ifi 0.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=203182&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Organization==
In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets.

While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:

* Introduction to the practical machine learning pipeline
* Exploratory data analysis
* The Python Data Science stack
* How to deal with unbalanced data
* Advanced algorithms for Data Science (an overview of competition winning algorithms)
* Parameter tuning for predictive models

Students need to submit their solutions to tasks by specific deadlines throughout the course. Note that this course thus requires a continous effort throughout the whole semester.
Solutions for each task have to be presented in class. A final report needs to be submitted at the end of the semester (March 31).

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Coursera Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.).
*Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries

==Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''When?'''
|{{Hl2}} |'''What?'''
|{{Hl2}} |'''Materials'''
|-
| align="right" | 19.10.2017
| Lecture 1: Introduction & The Data Science Pipeline - Task 1: Release
| [[Media:DS_1718_L1.pdf | Lecture Slides]] -- [https://user.informatik.uni-goettingen.de/~dkoll/courses/ds_pract/bike_sharing_ipynb.html Bike Sharing IPYNB] -- [https://www.kaggle.com/c/m-inf-1800-ws-17-18-task-1 Kaggle inClass Competition for Task 1]
|-
| align="right" | 26.10.2017
| Lecture 2: The Python Data Science Stack
|
|-
| align="right" | 02.11.2017
| Task 1: Intermediate meeting
|
|-
| align="right" | 09.11.2017
| No lecture
|
|-
| align="right" | 16.11.2017
| Task 1: Presentations // Task 2: Release
|
|-
| align="right" | 23.11.2017
| Lecture 3: Advanced Algorithms for Data Science
|
|-
| align="right" | 30.11.2017
| Lecture 4: Evaluation and Tuning of Models
|
|-
| align="right" | 07.12.2017
| Task 2: Intermediate meeting
|
|-
| align="right" | 14.12.2017
| No lecture
|
|-
| align="right" | 21.12.2017
| Task 2: Presentations // Task 3: Release
|
|-
| align="right" | 04.01.2018
| No lecture
|
|-
| align="right" | 11.01.2018
| Task 3: Intermediate meeting I
|
|-
| align="right" | 18.01.2018
| No lecture
|
|-
| align="right" | 25.01.2018
| Task 3: Intermediate meeting II
|
|-
| align="right" | 01.02.2018
| No lecture
|
|-
| align="right" | 08.02.2018-22.02.2018
| Task 3: Presentations
|
|-
| align="right" | 31.03.2018
| Final Report deadline
|
|-
|}

Advanced Practical Course Data Science for Computer Networks (Winter 2017/2018)

2017-10-26T14:11:49Z

Dkoll:

{{Announcement|Note: The room for this course has changed to the bigger room 2.101!)}}

{{Announcement|Note: The primary platform for communication in this course will be StudIP. Please register for the course there.)}}

== Details ==
{{CourseDetails
|credits=180h, 6 ECTS
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/david_koll Dr. David Koll]
|ta=None
|time=Thursday, 14-16
|place=Ifi 0.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=203182&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Organization==
In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets.

While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:

* Introduction to the practical machine learning pipeline
* Exploratory data analysis
* The Python Data Science stack
* How to deal with unbalanced data
* Advanced algorithms for Data Science (an overview of competition winning algorithms)
* Parameter tuning for predictive models

Students need to submit their solutions to tasks by specific deadlines throughout the course. Note that this course thus requires a continous effort throughout the whole semester.
Solutions for each task have to be presented in class. A final report needs to be submitted at the end of the semester (March 31).

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Coursera Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.).
*Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries

==Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''When?'''
|{{Hl2}} |'''What?'''
|{{Hl2}} |'''Materials'''
|-
| align="right" | 19.10.2017
| Lecture 1: Introduction & The Data Science Pipeline - Task 1: Release
| [[Media:DS_1718_L1.pdf | Lecture Slides]] -- [https://user.informatik.uni-goettingen.de/~dkoll/courses/ds_pract/bike_sharing_ipynb.html Bike Sharing IPYNB] -- [https://www.kaggle.com/c/m-inf-1800-ws-17-18-task-1 Kaggle inClass Competition for Task 1]
|-
| align="right" | 26.10.2017
| Lecture 2: The Python Data Science Stack
|
|-
| align="right" | 02.11.2017
| Task 1: Intermediate meeting
|
|-
| align="right" | 09.11.2017
| No lecture
|
|-
| align="right" | 16.11.2017
| Task 1: Presentations // Task 2: Release
|
|-
| align="right" | 23.11.2017
| Lecture 3: Advanced Algorithms for Data Science
|
|-
| align="right" | 30.11.2017
| Lecture 4: Evaluation and Tuning of Models
|
|-
| align="right" | 07.12.2017
| Task 2: Intermediate meeting
|
|-
| align="right" | 14.12.2017
| No lecture
|
|-
| align="right" | 21.12.2017
| Task 2: Presentations // Task 3: Release
|
|-
| align="right" | 04.01.2018
| No lecture
|
|-
| align="right" | 11.01.2018
| Task 3: Intermediate meeting I
|
|-
| align="right" | 18.01.2018
| No lecture
|
|-
| align="right" | 25.01.2018
| Task 3: Intermediate meeting II
|
|-
| align="right" | 01.02.2018
| No lecture
|
|-
| align="right" | 08.02.2018-22.02.2018
| Task 3: Presentations
|
|-
| align="right" | 31.03.2018
| Final Report deadline
|
|-
|}

Advanced Practical Course Data Science for Computer Networks (Winter 2017/2018)

2017-10-26T10:00:13Z

Dkoll:

{{Announcement|Note: The room for this course has changed to the bigger room 2.101!)}}

{{Announcement|Note: The primary platform for communication in this course will be StudIP. Please register for the course there.)}}

== Details ==
{{CourseDetails
|credits=180h, 6 ECTS
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/david_koll Dr. David Koll]
|ta=None
|time=Thursday, 14-16
|place=Ifi 2.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=203182&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Organization==
In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets.

While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:

* Introduction to the practical machine learning pipeline
* Exploratory data analysis
* The Python Data Science stack
* How to deal with unbalanced data
* Advanced algorithms for Data Science (an overview of competition winning algorithms)
* Parameter tuning for predictive models

Students need to submit their solutions to tasks by specific deadlines throughout the course. Note that this course thus requires a continous effort throughout the whole semester.
Solutions for each task have to be presented in class. A final report needs to be submitted at the end of the semester (March 31).

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Coursera Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.).
*Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries

==Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''When?'''
|{{Hl2}} |'''What?'''
|{{Hl2}} |'''Materials'''
|-
| align="right" | 19.10.2017
| Lecture 1: Introduction & The Data Science Pipeline - Task 1: Release
| [[Media:DS_1718_L1.pdf | Lecture Slides]] -- [https://user.informatik.uni-goettingen.de/~dkoll/courses/ds_pract/bike_sharing_ipynb.html Bike Sharing IPYNB] -- [https://www.kaggle.com/c/m-inf-1800-ws-17-18-task-1 Kaggle inClass Competition for Task 1]
|-
| align="right" | 26.10.2017
| Lecture 2: The Python Data Science Stack
|
|-
| align="right" | 02.11.2017
| Task 1: Intermediate meeting
|
|-
| align="right" | 09.11.2017
| No lecture
|
|-
| align="right" | 16.11.2017
| Task 1: Presentations // Task 2: Release
|
|-
| align="right" | 23.11.2017
| Lecture 3: Advanced Algorithms for Data Science
|
|-
| align="right" | 30.11.2017
| Lecture 4: Evaluation and Tuning of Models
|
|-
| align="right" | 07.12.2017
| Task 2: Intermediate meeting
|
|-
| align="right" | 14.12.2017
| No lecture
|
|-
| align="right" | 21.12.2017
| Task 2: Presentations // Task 3: Release
|
|-
| align="right" | 04.01.2018
| No lecture
|
|-
| align="right" | 11.01.2018
| Task 3: Intermediate meeting I
|
|-
| align="right" | 18.01.2018
| No lecture
|
|-
| align="right" | 25.01.2018
| Task 3: Intermediate meeting II
|
|-
| align="right" | 01.02.2018
| No lecture
|
|-
| align="right" | 08.02.2018-22.02.2018
| Task 3: Presentations
|
|-
| align="right" | 31.03.2018
| Final Report deadline
|
|-
|}

Software-defined Networking (Winter 2017/2018)

2017-10-24T10:51:41Z

Dkoll: /* Course Overview */

{{Announcement| See below for instructions to submit the exercises.}}.
{{Announcement| The final written exam will be on Friday, December 8th, 13:00 in "Provisorischer Hörsaal A" (chemistry building north campus)}}

== Details ==

{{CourseDetails
|credits=150h, 5 ECTS
|module=AI: M.Inf.1130: Software-defined Networks (SDN); ITIS: 3.31
|lecturer=[http://user.informatik.uni-goettingen.de/~dkoll Dr. David Koll]; [https://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai?lang=de Dr. Mayutan Arumaithurai]
|ta=[https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto M.Sc. Jacopo De Benedetto]
|time=9 October - 13 October 2017
|place=IFI 2.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=202348&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Overview==
Software-defined networking (SDN) has recently attracted both researchers in academia and big players in communication technologies,
and is currently probably the 'hottest' topic in computer networking.
This course will introduce SDN in both its theoretical concepts as well as in practical hands-on lectures, in which students will be required to implement SDN applications.

Note: For this course, basic proficiency in the Python programming language is required.

==Schedule==

{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''Type'''
|{{Hl2}} |'''Date'''
|{{Hl2}} |'''Morning Session I'''
|{{Hl2}} |'''Morning Session II'''
|{{Hl2}} |'''Afternoon Session I'''
|{{Hl2}} |'''Afternoon Session II'''
|-
|
| align="right" | '''Time'''
| 9:15 - 10:45
| 11:00 - 12:30
| 14:00 - 15:30
| 15:45 - 17.15
|-
|-
|'' Theory''
| align="right" |'''09.10.2017'''
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/I_SDN_Intro.pdf Lecture I: Introduction to SDN]
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/II_SDN_OpenFlow.pdf Lecture II: OpenFlow and its Applications]
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/III_SDN_NetVirt.pdf Lecture III: Network Virtualization via SDN]
| Exercise for lecture [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/1_SDN_Intro.pdf I] + [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/2_SDN_OpenFlow.pdf II]
|-
| ''Theory''
| align="right" | '''10.10.2017'''
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/IV_SDN_Controllers.pdf Lecture IV: SDN Controllers]
| Exercise for lecture [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/3_SDN_Virtualization.pdf III] + [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/4_SDN_Controllers.pdf IV]
| [https://drive.google.com/open?id=0B6KjNnPdhIrqZUVGcTJuaXhIcE0 Tutorial I]
| [https://drive.google.com/open?id=0B6KjNnPdhIrqZFh6U0tEUlR3NDQ Intro] [https://drive.google.com/open?id=0B6KjNnPdhIrqNVVpS2l1Yk5lcW8 Lab I]
|-
| ''Practical''
| align="right" | '''11.10.2017'''
| [https://drive.google.com/open?id=0B6KjNnPdhIrqd2xXd1AzTnBTblk Tutorial II]
| [https://drive.google.com/open?id=0B6KjNnPdhIrqSEtuYWozOWlyM0k Lab II]
| [https://drive.google.com/open?id=0B6KjNnPdhIrqNTZwVG5wNEk3dmc Tutorial III]
| [https://drive.google.com/open?id=0B6KjNnPdhIrqc0Q4MUZyNDJQVmM Lab III]
|-
| ''Practical''
| align="right" |'''12.10.2017'''
| Lecture V: Enhanced Data Plane I [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/middleboxes.pdf Middleboxes part-I][https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/FlowTags.pdf Flowtags]
| Lecture VI: Enhanced Data Plane II [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/middleboxes.pdf Middleboxes part-II]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab IV]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab V]
|-
|'' Theory/Practical''
| align="right" | '''13.10.2017'''
| Lecture VII: Northbound API [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/Northbound_API_Motivation.pdf Northbound_Motivation] [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/Northbound_API_Pyretic.pdf Pyretic] [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/p4_mayutan.pdf p4]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab VI]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab VII]
| Exercise for Lectures V, VI, VII [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/5_Data_plane_and_Northbound_API.pdf]
|-
|}

==Instructions to submit the exercises==

Please put all the exercises in a zip file and send it to Jacopo (jacopo.de-benedetto at cs.uni-goettingen.de). Those who have already sent it to Sameer, it is fine.

'''Passing requirement: Earn 50% of the points on each of the exercises'''

To know which exercises have you already submitted please consult this [https://drive.google.com/open?id=1rY3jIljgeOEdg_v1gjqzZS6oUc7FL7f4LEn6AAZrP-A list]

[[Category:Courses]]

Software-defined Networking (Winter 2017/2018)

2017-10-24T10:51:27Z

Dkoll:

{{Announcement| See below for instructions to submit the exercises.}}.
{{Announcement| The final written exam will be on Friday, December 8th, 13:00 in "Provisorischer Hörsaal A" (chemistry building north campus)}}

== Details ==

{{CourseDetails
|credits=150h, 5 ECTS
|module=AI: M.Inf.1130: Software-defined Networks (SDN); ITIS: 3.31
|lecturer=[http://user.informatik.uni-goettingen.de/~dkoll Dr. David Koll]; [https://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai?lang=de Dr. Mayutan Arumaithurai]
|ta=[https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto M.Sc. Jacopo De Benedetto]
|time=9 October - 13 October 2017
|place=IFI 2.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=202348&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Overview==
Software-defined networking (SDN) has recently attracted both researchers in academia and big players in communication technologies,
and is currently probably the 'hottest' topic in computer networking.
This course will introduce SDN in both its theoretical concepts as well as in practical hands-on lectures, in which students will be required to implement SDN applications.

{{Announcement| Unlike previous editions, this edition of the SDN block course will be for 5 days and an examination will be held in late November, early December (The examination date will be announced soon). }}.

Note: For this course, basic proficiency in the Python programming language is required.

==Schedule==

{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''Type'''
|{{Hl2}} |'''Date'''
|{{Hl2}} |'''Morning Session I'''
|{{Hl2}} |'''Morning Session II'''
|{{Hl2}} |'''Afternoon Session I'''
|{{Hl2}} |'''Afternoon Session II'''
|-
|
| align="right" | '''Time'''
| 9:15 - 10:45
| 11:00 - 12:30
| 14:00 - 15:30
| 15:45 - 17.15
|-
|-
|'' Theory''
| align="right" |'''09.10.2017'''
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/I_SDN_Intro.pdf Lecture I: Introduction to SDN]
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/II_SDN_OpenFlow.pdf Lecture II: OpenFlow and its Applications]
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/III_SDN_NetVirt.pdf Lecture III: Network Virtualization via SDN]
| Exercise for lecture [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/1_SDN_Intro.pdf I] + [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/2_SDN_OpenFlow.pdf II]
|-
| ''Theory''
| align="right" | '''10.10.2017'''
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/IV_SDN_Controllers.pdf Lecture IV: SDN Controllers]
| Exercise for lecture [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/3_SDN_Virtualization.pdf III] + [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/4_SDN_Controllers.pdf IV]
| [https://drive.google.com/open?id=0B6KjNnPdhIrqZUVGcTJuaXhIcE0 Tutorial I]
| [https://drive.google.com/open?id=0B6KjNnPdhIrqZFh6U0tEUlR3NDQ Intro] [https://drive.google.com/open?id=0B6KjNnPdhIrqNVVpS2l1Yk5lcW8 Lab I]
|-
| ''Practical''
| align="right" | '''11.10.2017'''
| [https://drive.google.com/open?id=0B6KjNnPdhIrqd2xXd1AzTnBTblk Tutorial II]
| [https://drive.google.com/open?id=0B6KjNnPdhIrqSEtuYWozOWlyM0k Lab II]
| [https://drive.google.com/open?id=0B6KjNnPdhIrqNTZwVG5wNEk3dmc Tutorial III]
| [https://drive.google.com/open?id=0B6KjNnPdhIrqc0Q4MUZyNDJQVmM Lab III]
|-
| ''Practical''
| align="right" |'''12.10.2017'''
| Lecture V: Enhanced Data Plane I [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/middleboxes.pdf Middleboxes part-I][https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/FlowTags.pdf Flowtags]
| Lecture VI: Enhanced Data Plane II [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/middleboxes.pdf Middleboxes part-II]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab IV]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab V]
|-
|'' Theory/Practical''
| align="right" | '''13.10.2017'''
| Lecture VII: Northbound API [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/Northbound_API_Motivation.pdf Northbound_Motivation] [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/Northbound_API_Pyretic.pdf Pyretic] [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/p4_mayutan.pdf p4]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab VI]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab VII]
| Exercise for Lectures V, VI, VII [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/5_Data_plane_and_Northbound_API.pdf]
|-
|}

==Instructions to submit the exercises==

Please put all the exercises in a zip file and send it to Jacopo (jacopo.de-benedetto at cs.uni-goettingen.de). Those who have already sent it to Sameer, it is fine.

'''Passing requirement: Earn 50% of the points on each of the exercises'''

To know which exercises have you already submitted please consult this [https://drive.google.com/open?id=1rY3jIljgeOEdg_v1gjqzZS6oUc7FL7f4LEn6AAZrP-A list]

[[Category:Courses]]

Software-defined Networking (Winter 2017/2018)

2017-10-24T10:51:13Z

Dkoll:

{{Announcement| See below for instructions to submit the exercises.}}.

{{Announcement| The final written exam will be on Friday, December 8th, 13:00 in "Provisorischer Hörsaal A" (chemistry building north campus)}}

== Details ==

{{CourseDetails
|credits=150h, 5 ECTS
|module=AI: M.Inf.1130: Software-defined Networks (SDN); ITIS: 3.31
|lecturer=[http://user.informatik.uni-goettingen.de/~dkoll Dr. David Koll]; [https://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai?lang=de Dr. Mayutan Arumaithurai]
|ta=[https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto M.Sc. Jacopo De Benedetto]
|time=9 October - 13 October 2017
|place=IFI 2.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=202348&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Overview==
Software-defined networking (SDN) has recently attracted both researchers in academia and big players in communication technologies,
and is currently probably the 'hottest' topic in computer networking.
This course will introduce SDN in both its theoretical concepts as well as in practical hands-on lectures, in which students will be required to implement SDN applications.

{{Announcement| Unlike previous editions, this edition of the SDN block course will be for 5 days and an examination will be held in late November, early December (The examination date will be announced soon). }}.

Note: For this course, basic proficiency in the Python programming language is required.

==Schedule==

{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''Type'''
|{{Hl2}} |'''Date'''
|{{Hl2}} |'''Morning Session I'''
|{{Hl2}} |'''Morning Session II'''
|{{Hl2}} |'''Afternoon Session I'''
|{{Hl2}} |'''Afternoon Session II'''
|-
|
| align="right" | '''Time'''
| 9:15 - 10:45
| 11:00 - 12:30
| 14:00 - 15:30
| 15:45 - 17.15
|-
|-
|'' Theory''
| align="right" |'''09.10.2017'''
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/I_SDN_Intro.pdf Lecture I: Introduction to SDN]
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/II_SDN_OpenFlow.pdf Lecture II: OpenFlow and its Applications]
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/III_SDN_NetVirt.pdf Lecture III: Network Virtualization via SDN]
| Exercise for lecture [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/1_SDN_Intro.pdf I] + [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/2_SDN_OpenFlow.pdf II]
|-
| ''Theory''
| align="right" | '''10.10.2017'''
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/IV_SDN_Controllers.pdf Lecture IV: SDN Controllers]
| Exercise for lecture [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/3_SDN_Virtualization.pdf III] + [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/4_SDN_Controllers.pdf IV]
| [https://drive.google.com/open?id=0B6KjNnPdhIrqZUVGcTJuaXhIcE0 Tutorial I]
| [https://drive.google.com/open?id=0B6KjNnPdhIrqZFh6U0tEUlR3NDQ Intro] [https://drive.google.com/open?id=0B6KjNnPdhIrqNVVpS2l1Yk5lcW8 Lab I]
|-
| ''Practical''
| align="right" | '''11.10.2017'''
| [https://drive.google.com/open?id=0B6KjNnPdhIrqd2xXd1AzTnBTblk Tutorial II]
| [https://drive.google.com/open?id=0B6KjNnPdhIrqSEtuYWozOWlyM0k Lab II]
| [https://drive.google.com/open?id=0B6KjNnPdhIrqNTZwVG5wNEk3dmc Tutorial III]
| [https://drive.google.com/open?id=0B6KjNnPdhIrqc0Q4MUZyNDJQVmM Lab III]
|-
| ''Practical''
| align="right" |'''12.10.2017'''
| Lecture V: Enhanced Data Plane I [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/middleboxes.pdf Middleboxes part-I][https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/FlowTags.pdf Flowtags]
| Lecture VI: Enhanced Data Plane II [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/middleboxes.pdf Middleboxes part-II]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab IV]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab V]
|-
|'' Theory/Practical''
| align="right" | '''13.10.2017'''
| Lecture VII: Northbound API [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/Northbound_API_Motivation.pdf Northbound_Motivation] [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/Northbound_API_Pyretic.pdf Pyretic] [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/p4_mayutan.pdf p4]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab VI]
| [https://wiki.net.informatik.uni-goettingen.de/wiki/Sdn-exercises-mayutan Lab VII]
| Exercise for Lectures V, VI, VII [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/SDN/2017_2018_WS/exercises/5_Data_plane_and_Northbound_API.pdf]
|-
|}

==Instructions to submit the exercises==

Please put all the exercises in a zip file and send it to Jacopo (jacopo.de-benedetto at cs.uni-goettingen.de). Those who have already sent it to Sameer, it is fine.

'''Passing requirement: Earn 50% of the points on each of the exercises'''

To know which exercises have you already submitted please consult this [https://drive.google.com/open?id=1rY3jIljgeOEdg_v1gjqzZS6oUc7FL7f4LEn6AAZrP-A list]

[[Category:Courses]]

Advanced Practical Course Data Science for Computer Networks (Winter 2017/2018)

2017-10-19T14:56:48Z

Dkoll: /* Preliminary Schedule */

{{Announcement|Note: The primary platform for communication in this course will be StudIP. Please register for the course there.)}}

== Details ==
{{CourseDetails
|credits=180h, 6 ECTS
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/david_koll Dr. David Koll]
|ta=None
|time=Thursday, 14-16 (tentative)
|place=Ifi 3.101 (tentative)
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=203182&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Organization==
In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets.

While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:

* Introduction to the practical machine learning pipeline
* Exploratory data analysis
* The Python Data Science stack
* How to deal with unbalanced data
* Advanced algorithms for Data Science (an overview of competition winning algorithms)
* Parameter tuning for predictive models

Students need to submit their solutions to tasks by specific deadlines throughout the course. Note that this course thus requires a continous effort throughout the whole semester.
Solutions for each task have to be presented in class. A final report needs to be submitted at the end of the semester (March 31).

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Coursera Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.).
*Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries

==Preliminary Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''When?'''
|{{Hl2}} |'''What?'''
|{{Hl2}} |'''Materials'''
|-
| align="right" | 19.10.2017
| Lecture 1: Introduction & The Data Science Pipeline - Task 1: Release
| [[Media:DS_1718_L1.pdf | Lecture Slides]] -- [https://user.informatik.uni-goettingen.de/~dkoll/courses/ds_pract/bike_sharing_ipynb.html Bike Sharing IPYNB] -- [https://www.kaggle.com/c/m-inf-1800-ws-17-18-task-1 Kaggle inClass Competition for Task 1]
|-
| align="right" | 26.10.2017
| Lecture 2: The Python Data Science Stack
|
|-
| align="right" | 02.11.2017
| Task 1: Intermediate meeting
|
|-
| align="right" | 09.11.2017
| No lecture
|
|-
| align="right" | 16.11.2017
| Task 1: Presentations // Task 2: Release
|
|-
| align="right" | 23.11.2017
| Lecture 3: Advanced Algorithms for Data Science
|
|-
| align="right" | 30.11.2017
| Lecture 4: Evaluation and Tuning of Models
|
|-
| align="right" | 07.12.2017
| Task 2: Intermediate meeting
|
|-
| align="right" | 14.12.2017
| No lecture
|
|-
| align="right" | 21.12.2017
| Task 2: Presentations // Task 3: Release
|
|-
| align="right" | 04.01.2018
| No lecture
|
|-
| align="right" | 11.01.2018
| Task 3: Intermediate meeting I
|
|-
| align="right" | 18.01.2018
| No lecture
|
|-
| align="right" | 25.01.2018
| Task 3: Intermediate meeting II
|
|-
| align="right" | 01.02.2018
| No lecture
|
|-
| align="right" | 08.02.2018-22.02.2018
| Task 3: Presentations
|
|-
| align="right" | 31.03.2018
| Final Report deadline
|
|-
|}

Advanced Practical Course Data Science for Computer Networks (Winter 2017/2018)

2017-10-19T14:56:03Z

Dkoll:

{{Announcement|Note: The primary platform for communication in this course will be StudIP. Please register for the course there.)}}

== Details ==
{{CourseDetails
|credits=180h, 6 ECTS
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/david_koll Dr. David Koll]
|ta=None
|time=Thursday, 14-16 (tentative)
|place=Ifi 3.101 (tentative)
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=203182&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Organization==
In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets.

While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:

* Introduction to the practical machine learning pipeline
* Exploratory data analysis
* The Python Data Science stack
* How to deal with unbalanced data
* Advanced algorithms for Data Science (an overview of competition winning algorithms)
* Parameter tuning for predictive models

Students need to submit their solutions to tasks by specific deadlines throughout the course. Note that this course thus requires a continous effort throughout the whole semester.
Solutions for each task have to be presented in class. A final report needs to be submitted at the end of the semester (March 31).

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Coursera Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.).
*Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries

==Preliminary Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''When?'''
|{{Hl2}} |'''What?'''
|{{Hl2}} |'''Materials'''
|-
| align="right" | 19.10.2017
| Lecture 1: Introduction & The Data Science Pipeline - Task 1: Release
| [[Media:DS_1718_L1.pdf | Lecture Slides]] -- [https://user.informatik.uni-goettingen.de/~dkoll/courses/ds_pract/bike_sharing_ipynb.html Bike Sharing IPYNB]
|-
| align="right" | 26.10.2017
| Lecture 2: The Python Data Science Stack
|
|-
| align="right" | 02.11.2017
| Task 1: Intermediate meeting
|
|-
| align="right" | 09.11.2017
| No lecture
|
|-
| align="right" | 16.11.2017
| Task 1: Presentations // Task 2: Release
|
|-
| align="right" | 23.11.2017
| Lecture 3: Advanced Algorithms for Data Science
|
|-
| align="right" | 30.11.2017
| Lecture 4: Evaluation and Tuning of Models
|
|-
| align="right" | 07.12.2017
| Task 2: Intermediate meeting
|
|-
| align="right" | 14.12.2017
| No lecture
|
|-
| align="right" | 21.12.2017
| Task 2: Presentations // Task 3: Release
|
|-
| align="right" | 04.01.2018
| No lecture
|
|-
| align="right" | 11.01.2018
| Task 3: Intermediate meeting I
|
|-
| align="right" | 18.01.2018
| No lecture
|
|-
| align="right" | 25.01.2018
| Task 3: Intermediate meeting II
|
|-
| align="right" | 01.02.2018
| No lecture
|
|-
| align="right" | 08.02.2018-22.02.2018
| Task 3: Presentations
|
|-
| align="right" | 31.03.2018
| Final Report deadline
|
|-
|}

File:DS 1718 L1.pdf

2017-10-19T14:54:31Z

Dkoll:

Advanced Practical Course Data Science for Computer Networks (Winter 2017/2018)

2017-10-19T14:54:14Z

Dkoll: /* Preliminary Schedule */

== Details ==
{{CourseDetails
|credits=180h, 6 ECTS
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/david_koll Dr. David Koll]
|ta=None
|time=Thursday, 14-16 (tentative)
|place=Ifi 3.101 (tentative)
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=203182&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Organization==
In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets.

While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:

* Introduction to the practical machine learning pipeline
* Exploratory data analysis
* The Python Data Science stack
* How to deal with unbalanced data
* Advanced algorithms for Data Science (an overview of competition winning algorithms)
* Parameter tuning for predictive models

Students need to submit their solutions to tasks by specific deadlines throughout the course. Note that this course thus requires a continous effort throughout the whole semester.
Solutions for each task have to be presented in class. A final report needs to be submitted at the end of the semester (March 31).

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Coursera Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.).
*Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries

==Preliminary Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''When?'''
|{{Hl2}} |'''What?'''
|{{Hl2}} |'''Materials'''
|-
| align="right" | 19.10.2017
| Lecture 1: Introduction & The Data Science Pipeline - Task 1: Release
| [[Media:DS_1718_L1.pdf | Lecture Slides]] -- [https://user.informatik.uni-goettingen.de/~dkoll/courses/ds_pract/bike_sharing_ipynb.html Bike Sharing IPYNB]
|-
| align="right" | 26.10.2017
| Lecture 2: The Python Data Science Stack
|
|-
| align="right" | 02.11.2017
| Task 1: Intermediate meeting
|
|-
| align="right" | 09.11.2017
| No lecture
|
|-
| align="right" | 16.11.2017
| Task 1: Presentations // Task 2: Release
|
|-
| align="right" | 23.11.2017
| Lecture 3: Advanced Algorithms for Data Science
|
|-
| align="right" | 30.11.2017
| Lecture 4: Evaluation and Tuning of Models
|
|-
| align="right" | 07.12.2017
| Task 2: Intermediate meeting
|
|-
| align="right" | 14.12.2017
| No lecture
|
|-
| align="right" | 21.12.2017
| Task 2: Presentations // Task 3: Release
|
|-
| align="right" | 04.01.2018
| No lecture
|
|-
| align="right" | 11.01.2018
| Task 3: Intermediate meeting I
|
|-
| align="right" | 18.01.2018
| No lecture
|
|-
| align="right" | 25.01.2018
| Task 3: Intermediate meeting II
|
|-
| align="right" | 01.02.2018
| No lecture
|
|-
| align="right" | 08.02.2018-22.02.2018
| Task 3: Presentations
|
|-
| align="right" | 31.03.2018
| Final Report deadline
|
|-
|}

Seminar on Internet Technologies (Winter 2017/2018)

2017-10-17T12:00:11Z

Dkoll: /* Topics */

== Details ==

{{CourseDetails
|credits=5 ECTS (BSc/MSc AI); 5 (ITIS)
|module= M.Inf.1124 ''-or-'' B.Inf.1207/1208; ITIS Module 3.16: Selected Topics in Internet Technologies
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu]
|ta=[http://www.net.informatik.uni-goettingen.de/people/Tao_Zhao Tao Zhao]
|time=Oct 19, 16:00ct: Introduction Meeting
|place=IFI Building, Room 3.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=148938&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course description==

This course covers selected topics on the up-to-date Internet technologies and research. Each student takes a topic, does a presentation and writes a report on it. Besides the introduction meeting, there are no regular meetings, lectures or classes for this course. The purpose of this course is to familiarize the students with new technologies, enable independent study of a specific topic, and train presentation and writing skills.

The informational meeting at the beginning of the course will cover some guidelines on scientific presenting and writing.

Due to topic advisors' workload limitation, we could only provide limited topics, and the topic assignment will be on the basis of first come first serve principle. Please contact the topic advisor directly for the topic availability.

Note: Participants in the seminar only need to register the exam before the end of the course.

==Passing requirements==
*Actively and frequently participate in the project communication with your topic advisor
**This accounts for 20% of your grade.
* Present the selected topic (20 min. presentation + 10 min. Q&A).
** This accounts for 40% of your grade.
* Write a report on the selected topic (12-15 pages) (LaTeX Template:[ftp://ftp.springer.de/pub/tex/latex/llncs/latex2e/llncs2e.zip]).
** This accounts for 40% of your grade.
* Please check the [[#Schedule]] and adhere to it.

==Schedule==
* '''Oct. 19, 16:00ct''': Introduction meeting
* '''TBD''' : Deadline for registration
* '''TBD''' : Presentations
* '''Mar. 31, 2018, 23:59''': Deadline for submission of report (should be sent to the topic adviser!)

== Topics ==

{| align="center" class="wikitable sortable" {{Prettytable}}
|-
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Topic Advisor'''
|{{Hl2}} |'''Initial Readings'''
|-
| '''Strengths and Limitations of Visualization Libraries for Data Science''' (assigned to Hannah Rauterberg; partially practical)
One core aspect of Data Science is data visualization. For this task, data scientists can exploit a plethora of different visualization libraries in different programming languages.
The goal of this seminar topic is to work out advantages and disadvantages of each library and to show the key differences in practical examples based on a real-world dataset.
Please note that students interested in this topic should be confident programmers in one of Python or R, and additionally in JavaScript, and ideally bring along some practical experience in data analysis/data mining.
| [http://user.informatik.uni-goettingen.de/~dkoll David Koll]
| [http://www.kdnuggets.com/2015/05/21-essential-data-visualization-tools.html]
|-
| '''A survey of clustering algorithms'''
Clustering is the unsupervised learning algorithm which groups unlabeled data into similar sub-groups. The clustering problem has been addressed in many contexts (social network, structure biological network ..). In this topic, we review and compare different approach address this problem. There are two main “small topics”:
a, Non-model based algorithms: Kmeans, spectral clustering, DBSCAN ..
b, A probabilistic model-based algorithm: Expectation Maximization, Gibbs sampler for Gaussian mixture model.
There are some useful practical parts which help students apply algorithms in real data.
| Thach Nguyen (Chuong-Thach.Nguyen@mpibpc.mpg.de)
| [https://pdfs.semanticscholar.org/26f1/78dbb00630ce19cccb9840ea12dbe31801be.pdf][http://papers.nips.cc/paper/2092-on-spectral-clustering-analysis-and-an-algorithm.pdf]
|-
| '''Transfer Learning for Visual Categorization (assigned to Shaheer Asghar)'''
Regular machine learning and data mining techniques study the training data for future inferences under a major assumption that the future data are within the same feature space or have the same distribution as the training data. However, due to the limited availability of human labeled training data, training data that stay in the same feature space or have the same distribution as the future data cannot be guaranteed to be sufficient enough to avoid the over-fitting problem. In real-world applications, apart from data in the target domain, related data in a different domain can also be included to expand the availability of our prior knowledge about the target future data. Transfer learning addresses such cross-domain learning problems by extracting useful information from data in a related domain and transferring them for being used in target tasks. In this work, this task is to provide a comprehensive study of state-of-the-art transfer learning algorithms in visual categorization applications, such as object recognition, image classification, and human action recognition. Note that this topic requires a comparatively high reading effort.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| [http://ieeexplore.ieee.org/abstract/document/6847217/]
|-
| '''A Survey on Semi-Supervised Learning Techniques (Assigned to Yifan Chen)'''
Semisupervised learning is a learning standard which deals with the study of how computers and natural systems such as human beings acquire knowledge in the presence of both labeled and unlabeled data. Semisupervised learning based methods are preferred when compared to the supervised and unsupervised learning because of the improved performance shown by the semisupervised approaches in the presence of large volumes of data. Labels are very hard to attain while unlabeled data are surplus, therefore semisupervised learning is a noble indication to shrink human labor and improve accuracy. In this work, this task is to survey some of the key approaches for semi-supervised learning. Note that this topic requires a comparatively high reading effort.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| [https://arxiv.org/abs/1402.4645]
|-
| '''A Survey on Multi-view Learning'''
In recent years, a great many methods of learning from multi-view data by considering the diversity of different views have been proposed. These views may be obtained from multiple sources or different feature subsets. In this work, this task is to survey a number of representative multi-view learning algorithms in different areas and organize and highlight similarities and differences between the variety of multi-view learning approaches. Note that this topic requires a comparatively high reading effort.
| [http://www.net.informatik.uni-goettingen.de/people/tao_zhao Tao Zhao]
| [https://arxiv.org/abs/1304.5634]
|-
| '''Industrie 4.0: Networking prospective and challenges (assigned to Tetiana Tolmachova)'''
Germany is targeting reach Industry 4.0 stage in factories. You should survey all requirements from networking prospective and the main challenges.
'''NOTE:'''This topic could be a good entry for master project and thesis later.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
|[http://www.plattform-i40.de/I40/Navigation/DE/Home/home.html][https://en.wikipedia.org/wiki/Industry_4.0][https://www.bmbf.de/de/zukunftsprojekt-industrie-4-0-848.html]
|-
| '''Segment Routing - a Survey'''
Segment Routing or SPRING project is getting more attention to the advantages that it promised to deliver. Initial demos on top of MPLS and IPv6 show big impact on terms of scalability, simplicity and performance. You should concentrate on SRv6 and SDN integration.
'''NOTE:'''This topic could be a good entry for master project and thesis later.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
|[http://www.segment-routing.net/][https://www.youtube.com/watch?v=BEo5MdB3o3Y][http://ieeexplore.ieee.org/abstract/document/7417124/]
|-
| '''Open Topic'''
This is one slot which is open for any student who has an idea on a new Internet Technology. This idea should not be addressed in the course in the last two years and related some how to the computer networks. To win with this slot, simply write me a short description of the technology and state three main references which you will use later for research.
| [http://www.net.informatik.uni-goettingen.de/people/osamah_barakat Osamah Barakat]
|
|-
| '''A Review of Relational Machine Learning for Knowledge Graphs (Assigned to Waqar Alamgir)'''
Traditional machine learning algorithms take as input a feature vector, which represents an object in terms of numeric or categorical attributes. The main learning task is to learn a mapping from this feature vector to an output prediction of some form. In Statistical Relational Learning (SRL), the representation of an object can contain its relationships to other objects. Thus the data is in the form of a graph, consisting of nodes (entities) and labelled edges (relationships between entities). The main goals of SRL include prediction of missing edges, prediction of properties of nodes, and clustering nodes based on their connectivity patterns. The task is to review a variety of techniques from the SRL community and explain how they can be applied to large-scale knowledge graphs (KGs), i.e., graph structured knowledge bases (KBs) that store factual information in form of relationships between entities.
|Bo Zhao (bo.zhao@gwdg.de)
|[http://ieeexplore.ieee.org/document/7358050/]
|-
| '''Deep Learning (Assigned to Fawad Abbasi)'''
Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech. The main task is to summarize some representative application scenarios of deep learning in big data analysis.
|Bo Zhao (bo.zhao@gwdg.de)
|[http://www.nature.com/nature/journal/v521/n7553/abs/nature14539.html?foxtrotcallback=true][http://dl.acm.org/citation.cfm?id=3092831]
|-
| '''Parallel Processing Systems for Big Data'''
The volume, variety, and velocity properties of big data and the valuable information it contains have motivated the investigation of many new parallel data processing systems in addition to the approaches using traditional database management systems (DBMSs). The task is to explore new research opportunities and assist users in selecting suitable processing systems for specific applications, considering the existing parallel data processing systems categorized by the data input as batch processing, stream processing, graph processing, and machine learning processing and introduce representative projects in each category.
|Bo Zhao (bo.zhao@gwdg.de)
|[http://ieeexplore.ieee.org/abstract/document/7547948/]
|-
| '''Towards SDN and NFV Fault Management and High Availability'''
Network Function Virtualisation (NFV), is gaining rapid momentum, but are they reliable? can they conform with the Telecom operators latency and availability requirements of Fine Nines or Six Nines? The focus of this work is to first study and understand the concerns with NFV in terms of their failures, what amount of availability can they support. Second, study the state-of-the-art in terms of techniques that have been provided in the Cloud and Data Center networks for the traditional Virtual Machine based approaches and make the clear distinction of what aspects can and cannot be adapted? and what are the characteristics of NFV that make them differ from traditional VM based solutions? and aspects and solutions that can be adapted to achieve scalability, efficiency, and reliability in the NFV environments.

| [http://www.net.informatik.uni-goettingen.de/people/sameer_kulkarni Sameer Kulkarni]
| [http://www.etsi.org/deliver/etsi_gs/NFV-REL/001_099/002/01.01.01_60/gs_NFV-REL002v010101p.pdf] [https://portal.etsi.org/Portals/0/TBpages/NFV/Docs/NFV_White_Paper3.pdf] [https://datatracker.ietf.org/rg/nfvrg/documents/] [https://www.opnfv.org]
|-
|'''Service Plane for Network Functions: Network Service Headers and Other alternatives'''

Focus of this topic is to understand 'Service Function Chaining of Network Functions', the state-of-the-art proposals like Network Service Headers and related academic works. Reason and justify the need for service plane and then try to propose new mechanisms and design of the data plane to support network services, and the control plane functions necessary to manage these data plane functions.

| [http://www.net.informatik.uni-goettingen.de/people/sameer_kulkarni Sameer Kulkarni]
| [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6733615] [http://conferences2.sigcomm.org/acm-icn/2014/papers/p107.pdf] [https://tools.ietf.org/pdf/draft-quinn-sfc-nsh-07.pdf]
|-
| '''Online Convex Optimization Algorithms for Machine learning'''
Machine learning is a current buzz word in both industry and academia. The goal of this topic is to perform survey of online convex optimization algorithms used in machine learning. The goal is to present at least two usecases describing (at high level) usage of online convex optimization framework.
| [http://www.net.informatik.uni-goettingen.de/people/abhinandan%20s_prasad Abhinandan S Prasad]
| [http://www.cs.huji.ac.il/~shais/papers/OLsurvey.pdf][http://ocobook.cs.princeton.edu/OCObook.pdf]
|-
| '''Prediction Markets (assigned to Dia Adden)'''
Prediction markets are exchange-traded markets created for the purpose of trading the outcome of events. The market prices indicate the probability of an event. The goal is to study and understand how prediction markets work.
| [http://www.net.informatik.uni-goettingen.de/people/abhinandan%20s_prasad Abhinandan S Prasad]
| [https://en.wikipedia.org/wiki/Prediction_market][http://www.nature.com/news/the-power-of-prediction-markets-1.20820][https://dash.harvard.edu/handle/1/5027266]
|-
| '''Traffic Data Analysis --A survey (assigned to Cheng Chang) '''
Great amount of traffic data are generated everyday from private cars, subway, taxi and buses, etc. Traffic data analysis is of great help to understand the patterns of people mobility, transport planning, urban management and policymaking. And it is also an interesting way to learn some basic knowledge about big data and machine learning.
| [Shichang Ding--shichang.ding@informatik.uni-goettingen.de]
| [http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0149222][https://pdfs.semanticscholar.org/7d15/0a9390d569750978d9abcee4524f1974961f.pdf]
|-
| '''Fuctional Zone Discovery inside Cities -- A survey'''
Modern big cities usually consists of different functional regions, for example: Wall Street is famous for business district while Broadway is well know as an entertainment street. Discovering functional regions can help understand the economic, physical and social characters of a city, and is important to applications like:urban planning, advertising, tourism recommendation, business site selection, etc. It can help you better understand some very useful techniques of data mining, machine learning and etc.
| [Shichang Ding--shichang.ding@informatik.uni-goettingen.de]
| [https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/funcZone_TKDE_Zheng.pdf][http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.462.2440&rep=rep1&type=pdf]
|-
| '''Human Trajectory Clustering -- A survey'''
A trajectory is a sequence of the location and timestamp of a moving object. It is not only an important type of spatio-temporal data, but also a critical source of information. Extracting patterns from different tra-
jectory data can help people understand the drives and outcomes of individual and collective spatial dynamics,such as human behavior patterns, transport and logistics, emergency evacuation management, animal behavior,
and marketing. Recently, a larger number of trajectory data are available for analyzing the temporal and spatial pattern, as the result of the improvements of tracking facilities and sensor networks. Therefore, clustering analysis needs to be used to find the implicit patterns in it. In this topic, you need to read and conclude knowledge from several important papers about human trajectory clustering.
| [Shichang Ding--shichang.ding@informatik.uni-goettingen.de]
| [https://www.ideals.illinois.edu/bitstream/handle/2142/11301/Trajectory%20Clustering%20A%20Partition-and-Group%20Framework.pdf?sequence=2&isAllowed=y]
|-
| '''Adaptive Video Streaming ''' (Assigned to: Muhammad Salman Gurmani)
Today's Internet is a heterogeneous networking environment. In such an environment, resources available to multimedia applications vary. To adapt to the changes in network conditions, both networking techniques and application layer techniques have been proposed. The study must give an overview of the different techniques proposed and some real use-case scenarios (ever heard about a company named Netflix??)
| [https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto ]
| [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6913491] [https://perso.telecom-paristech.fr/~drossi/paper/icn_das_techrep.pdf] [https://www-users.cs.umn.edu/~viadhi/netflix.pdf]
|-
| '''D2D Proximity Services'''
Sometimes referred as "digital sixth sense", Device-to-device (D2D) proximity discovery enables spectral reuse via D2D communications as well as a range of innovative proximity services, such as enhanced social networking and location services, thus helping in the offload of local data transmission. The study involves analyzing the actual and experimental technological solutions that enables the proximity services and the underlying communication protocols.
'''NOTE:'''This topic could be a good entry for [https://wiki.net.informatik.uni-goettingen.de/wiki/Theses_and_Projects master project and thesis].
| [https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto ]
| [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6807945] [https://www.qualcomm.com/invention/research/projects/lte-direct] [https://www.wi-fi.org/discover-wi-fi/wi-fi-aware]
|-
| '''360-degree Videos & Virtual Reality'''
360-degree videos are video recordings where a view in every direction is recorded at the same time, shot using an omnidirectional camera or a collection of cameras. During playback the viewer has control of the viewing direction like a panorama. They are often associated with VR (Virtual Reality), where a person using special equipment is able to "look around" in an artificial world. This task consists in study the actual solutions and protocols that enables the transmission of 360-degree videos, highlighting the challenges related to an efficient transmission of the video stream.
'''NOTE: possiblity to extend the work for master project or thesis.
| [https://www.net.informatik.uni-goettingen.de/people/jacopo_de%20benedetto Jacopo De Benedetto ]
| [http://www.com583.com/files/Redefining%20The%20Axiom%20Of%20Story_%20The%20VR%20And%20360%20Video%20Complex%20_%20TechCrunch.pdf] [http://delivery.acm.org/10.1145/2990000/2980056/p1-qian.pdf?ip=134.76.81.35&id=2980056&acc=ACTIVE%20SERVICE&key=2BA2C432AB83DA15%2E8C14E74AF280C121%2E4D4702B0C3E38B35%2E4D4702B0C3E38B35&CFID=819974159&CFTOKEN=46402817&__acm__=1508238751_aa9aa8f7a54b27ba5cfa252d87c7d5df] [http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7823660]
|-
| '''Low-Rate Wireless Personal Area Networks''' (Assigned to: Asad Abbas)
The increasing number of smart devices and sensors deployed nowdays and their power and performance requires specific protocol communications. IEEE 802.15.4 is a technical standard which defines the operation of low-rate wireless personal area networks (LR-WPANs) and it is the basis for specifications like ZigBee, Thread, 6LowPan, LoRa and many others. The task of this topic is to give an overview of these standards and a comparison of the related specifications together with significant solution from both academy and industry. Personal proposal are very welcome (This can also be a starting point for a project/thesis).
| Sripriya Adhatarao (adhatarao@cs.uni-goettingen.de)
| [https://standards.ieee.org/findstds/standard/802.15.4-2015.html] [https://datatracker.ietf.org/wg/6lowpan/documents/] [https://www.lora-alliance.org/] [http://www.zigbee.org/] [http://threadgroup.org]
|-
| '''IoT with ICN''' (Assigned to : Md Tofiqul Islam)
IoT is a growing topic of Interest but existing technologies do not support the resource constrained devices efficiently. ICN is a promising new future Internet architecture and IoT can greatly benefit by using ICN. In this topic, you will explore the existing ICN proposals for IoT and will specifically work on naming challenges in IoT with ICN.
| Sripriya Adhatarao (adhatarao@cs.uni-goettingen.de)
| [https://standards.ieee.org/findstds/standard/802.15.4-2015.html] [https://datatracker.ietf.org/wg/6lowpan/documents/] [https://www.lora-alliance.org/] [http://www.zigbee.org/] [http://threadgroup.org]
|-
| '''Crawling the Internet''' (Assigned to : Hanna Holderied)
Many services specifically including Google use crawlers to systematically browse the Internet for Indexing and other purposes. In this task you will explore the different types of crawlers that exist in the internet and what are they used for. You will perform a research on how these crawlers work and what their results are used for. This topic can also lead to a potential Master project/thesis.
| Sripriya Adhatarao (adhatarao@cs.uni-goettingen.de)
|-
|}

==Workﬂow==

=== 1. Select a topic ===
A student picks a topic to work on. You can pick up a topic and start working '''at any time'''. However, make sure to notify the advisor of the topic before starting to work.

=== 2. Get your work advised ===
For each topic, a topic advisor is available. He is your contact person for questions and problems regarding the topic. He supports you as much as you want, so please do not hesitate to approach him for any advice or with any questions you might have. It is recommended (and not mandatory) that you schedule a face-to-face meeting with him right after you select your topic.

=== 3. Approach your topic ===

* By choosing a topic, you choose the direction of elaboration.
* You may work in different styles, for example:
** Survey: Basic introduction, overview of the ﬁeld; general problems, methods, approaches.
** Specific problem: Detailed introduction, details about the problem and the solution.
* You should include your own thoughts on your topic.

=== 4. Prepare your presentation ===

* Present your topic to the audience (in English).
* 20 minutes of presentation followed by 10 minutes discussion.

You present your topic to an audience of students and other interested people (usually the [http://www.net.informatik.uni-goettingen.de/ NET] group members). Your presentation should give the audience a general idea of the topic and highlight interesting problems and solutions. You have 20 minutes to present your topic followed by 10 minutes of discussion. You must keep it within the time limit. Please send your slides to your topic advisor for any possible feedback before your presentation.

Hints for preparing the presentation:
20 minutes are too short to present a topic fully.
It is alright to focus just on one certain important aspect.
Limit the introduction of basics.
Make sure to ﬁnish in time.

Suggestions for preparing the slides:
No more than 20 pages/slides.
Get your audiences to quickly understand the general idea.
Figures, tables and animations are better than sentences.
Summary of the topic: thinking in your own words.

=== 5. Write your report ===

* Present the problem with its background.
* Detail the approaches, techniques, methods to handle the problem.
* Evaluate and assess those approaches (e.g., pros and cons).
* Give a short outlook on potential future developments.

The report must be written in English according to common guidelines for scientific papers, between 12 and 15 pages of content (excluding the table of content, bibliography, etc.).

=== 6. Course schedule===
There are no regular meetings, lectures or classes for this course. The work is expected to be done by yourself with the assistance of your topic advisor. Please follow the [[#Schedule]] to take appropriate actions.

[[Category:Courses]]

Seminar on Internet Technologies (Winter 2017/2018)

2017-09-20T15:01:32Z

Dkoll: /* Topics */

Advanced Practical Course Data Science for Computer Networks (Winter 2017/2018)

2017-09-15T10:07:14Z

Dkoll: /* Prerequisites */

== Details ==
{{CourseDetails
|credits=180h, 6 ECTS
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/david_koll Dr. David Koll]
|ta=None
|time=Thursday, 14-16 (tentative)
|place=Ifi 3.101 (tentative)
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=203182&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Organization==
In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets.

While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:

* Introduction to the practical machine learning pipeline
* Exploratory data analysis
* The Python Data Science stack
* How to deal with unbalanced data
* Advanced algorithms for Data Science (an overview of competition winning algorithms)
* Parameter tuning for predictive models

Students need to submit their solutions to tasks by specific deadlines throughout the course. Note that this course thus requires a continous effort throughout the whole semester.
Solutions for each task have to be presented in class. A final report needs to be submitted at the end of the semester (March 31).

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Coursera Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.).
*Knowledge of any of the following languages: Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries

==Preliminary Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''When?'''
|{{Hl2}} |'''What?'''
|{{Hl2}} |'''Materials'''
|-
| align="right" | 19.10.2017
| Lecture 1: Introduction & The Data Science Pipeline - Task 1: Release
|
|-
| align="right" | 26.10.2017
| Lecture 2: The Python Data Science Stack
|
|-
| align="right" | 02.11.2017
| Task 1: Intermediate meeting
|
|-
| align="right" | 09.11.2017
| No lecture
|
|-
| align="right" | 16.11.2017
| Task 1: Presentations // Task 2: Release
|
|-
| align="right" | 23.11.2017
| Lecture 3: Advanced Algorithms for Data Science
|
|-
| align="right" | 30.11.2017
| Lecture 4: Evaluation and Tuning of Models
|
|-
| align="right" | 07.12.2017
| Task 2: Intermediate meeting
|
|-
| align="right" | 14.12.2017
| No lecture
|
|-
| align="right" | 21.12.2017
| Task 2: Presentations // Task 3: Release
|
|-
| align="right" | 04.01.2018
| No lecture
|
|-
| align="right" | 11.01.2018
| Task 3: Intermediate meeting I
|
|-
| align="right" | 18.01.2018
| No lecture
|
|-
| align="right" | 25.01.2018
| Task 3: Intermediate meeting II
|
|-
| align="right" | 01.02.2018
| No lecture
|
|-
| align="right" | 08.02.2018-22.02.2018
| Task 3: Presentations
|
|-
| align="right" | 31.03.2018
| Final Report deadline
|
|-
|}

Advanced Practical Course Data Science for Computer Networks (Winter 2017/2018)

2017-09-15T10:07:02Z

Dkoll: /* Prerequisites */

== Details ==
{{CourseDetails
|credits=180h, 6 ECTS
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/david_koll Dr. David Koll]
|ta=None
|time=Thursday, 14-16 (tentative)
|place=Ifi 3.101 (tentative)
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=203182&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Organization==
In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets.

While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:

* Introduction to the practical machine learning pipeline
* Exploratory data analysis
* The Python Data Science stack
* How to deal with unbalanced data
* Advanced algorithms for Data Science (an overview of competition winning algorithms)
* Parameter tuning for predictive models

Students need to submit their solutions to tasks by specific deadlines throughout the course. Note that this course thus requires a continous effort throughout the whole semester.
Solutions for each task have to be presented in class. A final report needs to be submitted at the end of the semester (March 31).

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Coursera Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.).
*Knowledge of any of Python (course language), R, JAVA, Matlab or any language that features proper machine learning libraries

==Preliminary Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''When?'''
|{{Hl2}} |'''What?'''
|{{Hl2}} |'''Materials'''
|-
| align="right" | 19.10.2017
| Lecture 1: Introduction & The Data Science Pipeline - Task 1: Release
|
|-
| align="right" | 26.10.2017
| Lecture 2: The Python Data Science Stack
|
|-
| align="right" | 02.11.2017
| Task 1: Intermediate meeting
|
|-
| align="right" | 09.11.2017
| No lecture
|
|-
| align="right" | 16.11.2017
| Task 1: Presentations // Task 2: Release
|
|-
| align="right" | 23.11.2017
| Lecture 3: Advanced Algorithms for Data Science
|
|-
| align="right" | 30.11.2017
| Lecture 4: Evaluation and Tuning of Models
|
|-
| align="right" | 07.12.2017
| Task 2: Intermediate meeting
|
|-
| align="right" | 14.12.2017
| No lecture
|
|-
| align="right" | 21.12.2017
| Task 2: Presentations // Task 3: Release
|
|-
| align="right" | 04.01.2018
| No lecture
|
|-
| align="right" | 11.01.2018
| Task 3: Intermediate meeting I
|
|-
| align="right" | 18.01.2018
| No lecture
|
|-
| align="right" | 25.01.2018
| Task 3: Intermediate meeting II
|
|-
| align="right" | 01.02.2018
| No lecture
|
|-
| align="right" | 08.02.2018-22.02.2018
| Task 3: Presentations
|
|-
| align="right" | 31.03.2018
| Final Report deadline
|
|-
|}

Advanced Practical Course Data Science for Computer Networks (Winter 2017/2018)

2017-09-15T10:06:24Z

Dkoll: /* Prerequisites */

== Details ==
{{CourseDetails
|credits=180h, 6 ECTS
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/david_koll Dr. David Koll]
|ta=None
|time=Thursday, 14-16 (tentative)
|place=Ifi 3.101 (tentative)
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=203182&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Organization==
In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets.

While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:

* Introduction to the practical machine learning pipeline
* Exploratory data analysis
* The Python Data Science stack
* How to deal with unbalanced data
* Advanced algorithms for Data Science (an overview of competition winning algorithms)
* Parameter tuning for predictive models

Students need to submit their solutions to tasks by specific deadlines throughout the course. Note that this course thus requires a continous effort throughout the whole semester.
Solutions for each task have to be presented in class. A final report needs to be submitted at the end of the semester (March 31).

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Coursera Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.).
*Knowledge of Python or R

==Preliminary Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''When?'''
|{{Hl2}} |'''What?'''
|{{Hl2}} |'''Materials'''
|-
| align="right" | 19.10.2017
| Lecture 1: Introduction & The Data Science Pipeline - Task 1: Release
|
|-
| align="right" | 26.10.2017
| Lecture 2: The Python Data Science Stack
|
|-
| align="right" | 02.11.2017
| Task 1: Intermediate meeting
|
|-
| align="right" | 09.11.2017
| No lecture
|
|-
| align="right" | 16.11.2017
| Task 1: Presentations // Task 2: Release
|
|-
| align="right" | 23.11.2017
| Lecture 3: Advanced Algorithms for Data Science
|
|-
| align="right" | 30.11.2017
| Lecture 4: Evaluation and Tuning of Models
|
|-
| align="right" | 07.12.2017
| Task 2: Intermediate meeting
|
|-
| align="right" | 14.12.2017
| No lecture
|
|-
| align="right" | 21.12.2017
| Task 2: Presentations // Task 3: Release
|
|-
| align="right" | 04.01.2018
| No lecture
|
|-
| align="right" | 11.01.2018
| Task 3: Intermediate meeting I
|
|-
| align="right" | 18.01.2018
| No lecture
|
|-
| align="right" | 25.01.2018
| Task 3: Intermediate meeting II
|
|-
| align="right" | 01.02.2018
| No lecture
|
|-
| align="right" | 08.02.2018-22.02.2018
| Task 3: Presentations
|
|-
| align="right" | 31.03.2018
| Final Report deadline
|
|-
|}

Advanced Practical Course Data Science for Computer Networks (Winter 2017/2018)

2017-09-13T11:17:35Z

Dkoll: /* Preliminary Schedule */

== Details ==
{{CourseDetails
|credits=180h, 6 ECTS
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/david_koll Dr. David Koll]
|ta=None
|time=Thursday, 14-16 (tentative)
|place=Ifi 3.101 (tentative)
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=203182&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Organization==
In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets.

While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:

* Introduction to the practical machine learning pipeline
* Exploratory data analysis
* The Python Data Science stack
* How to deal with unbalanced data
* Advanced algorithms for Data Science (an overview of competition winning algorithms)
* Parameter tuning for predictive models

Students need to submit their solutions to tasks by specific deadlines throughout the course. Note that this course thus requires a continous effort throughout the whole semester.
Solutions for each task have to be presented in class. A final report needs to be submitted at the end of the semester (March 31).

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Coursera Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.).
*Knowledge of Python or R...
*...and relevant libraries (e.g., SciKit Learn, Pandas, caret, etc.)

==Preliminary Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''When?'''
|{{Hl2}} |'''What?'''
|{{Hl2}} |'''Materials'''
|-
| align="right" | 19.10.2017
| Lecture 1: Introduction & The Data Science Pipeline - Task 1: Release
|
|-
| align="right" | 26.10.2017
| Lecture 2: The Python Data Science Stack
|
|-
| align="right" | 02.11.2017
| Task 1: Intermediate meeting
|
|-
| align="right" | 09.11.2017
| No lecture
|
|-
| align="right" | 16.11.2017
| Task 1: Presentations // Task 2: Release
|
|-
| align="right" | 23.11.2017
| Lecture 3: Advanced Algorithms for Data Science
|
|-
| align="right" | 30.11.2017
| Lecture 4: Evaluation and Tuning of Models
|
|-
| align="right" | 07.12.2017
| Task 2: Intermediate meeting
|
|-
| align="right" | 14.12.2017
| No lecture
|
|-
| align="right" | 21.12.2017
| Task 2: Presentations // Task 3: Release
|
|-
| align="right" | 04.01.2018
| No lecture
|
|-
| align="right" | 11.01.2018
| Task 3: Intermediate meeting I
|
|-
| align="right" | 18.01.2018
| No lecture
|
|-
| align="right" | 25.01.2018
| Task 3: Intermediate meeting II
|
|-
| align="right" | 01.02.2018
| No lecture
|
|-
| align="right" | 08.02.2018-22.02.2018
| Task 3: Presentations
|
|-
| align="right" | 31.03.2018
| Final Report deadline
|
|-
|}

Advanced Practical Course Data Science for Computer Networks (Winter 2017/2018)

2017-08-07T12:58:00Z

Dkoll: /* Preliminary Schedule */

== Details ==
{{CourseDetails
|credits=180h, 6 ECTS
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/david_koll Dr. David Koll]
|ta=None
|time=Thursday, 14-16 (tentative)
|place=Ifi 3.101 (tentative)
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=203182&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Organization==
In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets.

While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:

* Introduction to the practical machine learning pipeline
* Exploratory data analysis
* The Python Data Science stack
* How to deal with unbalanced data
* Advanced algorithms for Data Science (an overview of competition winning algorithms)
* Parameter tuning for predictive models

Students need to submit their solutions to tasks by specific deadlines throughout the course. Note that this course thus requires a continous effort throughout the whole semester.
Solutions for each task have to be presented in class. A final report needs to be submitted at the end of the semester (March 31).

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Coursera Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.).
*Knowledge of Python or R...
*...and relevant libraries (e.g., SciKit Learn, Pandas, caret, etc.)

==Preliminary Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''When?'''
|{{Hl2}} |'''What?'''
|{{Hl2}} |'''Materials'''
|-
| align="right" | 19.10.2017
| Lecture 1: Introduction & The Data Science Pipeline - Task 1: Release
|
|-
| align="right" | 26.10.2017
| Lecture 2: The Python Data Science Stack
|
|-
| align="right" | 02.11.2017
| Task 1: Intermediate meeting
|
|-
| align="right" | 09.11.2017
| No lecture
|
|-
| align="right" | 16.11.2017
| No lecture
|
|-
| align="right" | 23.11.2017
| Task 1: Presentations // Task 2: Release
|
|-
| align="right" | 30.11.2017
| Lecture 3: Advanced Algorithms for Data Science
|
|-
| align="right" | 07.12.2017
| Task 2: Intermediate meeting
|
|-
| align="right" | 14.12.2017
| Lecture 4: Evaluation and Tuning of Models
|
|-
| align="right" | 21.12.2017
| Task 2: Presentations // Task 3: Release
|
|-
| align="right" | 04.01.2018
| No lecture
|
|-
| align="right" | 11.01.2018
| Task 3: Intermediate meeting I
|
|-
| align="right" | 18.01.2018
| No lecture
|
|-
| align="right" | 25.01.2018
| Task 3: Intermediate meeting II
|
|-
| align="right" | 01.02.2018
| No lecture
|
|-
| align="right" | 08.02.2018-22.02.2018
| Task 3: Presentations
|
|-
| align="right" | 31.03.2018
| Final Report deadline
|
|-
|}

Advanced Practical Course Data Science for Computer Networks (Winter 2017/2018)

2017-08-07T12:57:43Z

Dkoll: /* Preliminary Schedule */

== Details ==
{{CourseDetails
|credits=180h, 6 ECTS
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/david_koll Dr. David Koll]
|ta=None
|time=Thursday, 14-16 (tentative)
|place=Ifi 3.101 (tentative)
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=203182&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Organization==
In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets.

While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:

* Introduction to the practical machine learning pipeline
* Exploratory data analysis
* The Python Data Science stack
* How to deal with unbalanced data
* Advanced algorithms for Data Science (an overview of competition winning algorithms)
* Parameter tuning for predictive models

Students need to submit their solutions to tasks by specific deadlines throughout the course. Note that this course thus requires a continous effort throughout the whole semester.
Solutions for each task have to be presented in class. A final report needs to be submitted at the end of the semester (March 31).

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Coursera Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.).
*Knowledge of Python or R...
*...and relevant libraries (e.g., SciKit Learn, Pandas, caret, etc.)

==Preliminary Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''When?'''
|{{Hl2}} |'''What?'''
|{{Hl2}} |'''Materials'''
|-
| align="right" | 19.10.2017
| Lecture 1: Introduction & The Data Science Pipeline - Task 1: Release
|
|-
| align="right" | 26.10.2017
| Lecture 2: The Python Data Science Stack
|
|-
| align="right" | 02.11.2017
| Task 1: Intermediate meeting
|
|-
| align="right" | 09.11.2017
| No lecture
|
|-
| align="right" | 16.11.2017
| No lecture
|
|-
| align="right" | 23.11.2017
| Task 1: Presentations // Task 2: Release
|
|-
| align="right" | 30.11.2017
| Lecture 3: Advanced algorithms for Data Science
|
|-
| align="right" | 07.12.2017
| Task 2: Intermediate meeting
|
|-
| align="right" | 14.12.2017
| Lecture 4: Evaluation and Tuning of Models
|
|-
| align="right" | 21.12.2017
| Task 2: Presentations // Task 3: Release
|
|-
| align="right" | 04.01.2018
| No lecture
|
|-
| align="right" | 11.01.2018
| Task 3: Intermediate meeting I
|
|-
| align="right" | 18.01.2018
| No lecture
|
|-
| align="right" | 25.01.2018
| Task 3: Intermediate meeting II
|
|-
| align="right" | 01.02.2018
| No lecture
|
|-
| align="right" | 08.02.2018-22.02.2018
| Task 3: Presentations
|
|-
| align="right" | 31.03.2018
| Final Report deadline
|
|-
|}

Advanced Practical Course Data Science for Computer Networks (Winter 2017/2018)

2017-08-07T12:57:12Z

Dkoll: /* Details */

== Details ==
{{CourseDetails
|credits=180h, 6 ECTS
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/david_koll Dr. David Koll]
|ta=None
|time=Thursday, 14-16 (tentative)
|place=Ifi 3.101 (tentative)
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=203182&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==Course Organization==
In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets.

While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:

* Introduction to the practical machine learning pipeline
* Exploratory data analysis
* The Python Data Science stack
* How to deal with unbalanced data
* Advanced algorithms for Data Science (an overview of competition winning algorithms)
* Parameter tuning for predictive models

Students need to submit their solutions to tasks by specific deadlines throughout the course. Note that this course thus requires a continous effort throughout the whole semester.
Solutions for each task have to be presented in class. A final report needs to be submitted at the end of the semester (March 31).

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Coursera Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.).
*Knowledge of Python or R...
*...and relevant libraries (e.g., SciKit Learn, Pandas, caret, etc.)

==Preliminary Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''When?'''
|{{Hl2}} |'''What?'''
|{{Hl2}} |'''Materials'''
|-
| align="right" | 19.10.2017
| Lecture 1: Introduction & The Data Science Pipeline
|
|-
| align="right" | 26.10.2017
| Lecture 2: The Python Data Science Stack
|
|-
| align="right" | 02.11.2017
| Task 1: Intermediate meeting
|
|-
| align="right" | 09.11.2017
| No lecture
|
|-
| align="right" | 16.11.2017
| No lecture
|
|-
| align="right" | 23.11.2017
| Task 1: Presentations // Task 2: Release
|
|-
| align="right" | 30.11.2017
| Lecture 3: Advanced algorithms for Data Science
|
|-
| align="right" | 07.12.2017
| Task 2: Intermediate meeting
|
|-
| align="right" | 14.12.2017
| Lecture 4: Evaluation and Tuning of Models
|
|-
| align="right" | 21.12.2017
| Task 2: Presentations // Task 3: Release
|
|-
| align="right" | 04.01.2018
| No lecture
|
|-
| align="right" | 11.01.2018
| Task 3: Intermediate meeting I
|
|-
| align="right" | 18.01.2018
| No lecture
|
|-
| align="right" | 25.01.2018
| Task 3: Intermediate meeting II
|
|-
| align="right" | 01.02.2018
| No lecture
|
|-
| align="right" | 08.02.2018-22.02.2018
| Task 3: Presentations
|
|-
| align="right" | 31.03.2018
| Final Report deadline
|
|-
|}

Software-defined Networking Registration

2017-08-07T12:55:34Z

Dkoll: /* Registered participants (I will remove these soon, Just having it temporarily here for people to see if their registration was successful) */

== Registered participants (I will remove these soon, Just having it temporarily here for people to see if their registration was successful) ==
*Fangxi Deng
*Hanna Holderied
*Joana Niermann
*Maxi Wess
*Mojtaba Shabani
*Pouya Saeedfar
*Shakik Ahmed Chowdhury
*Towfique Ahmed
*Zico Abhi Dey

== Waiting List ==

Software-defined Networking Registration

2017-08-07T12:55:19Z

Dkoll: /* Registered participants (I will remove these soon, Just having it temporarily here for people to see if their registration was successful) */

== Registered participants (I will remove these soon, Just having it temporarily here for people to see if their registration was successful) ==
Fangxi Deng
Hanna Holderied
Joana Niermann
Maxi Wess
Mojtaba Shabani
Pouya Saeedfar
Shakik Ahmed Chowdhury
Towfique Ahmed
Zico Abhi Dey

== Waiting List ==

Advanced Practical Course Data Science for Computer Networks (Winter 2017/2018)

2017-07-27T10:26:42Z

Dkoll: /* Course Organization */

== Details ==
{{CourseDetails
|credits=180h, 6 ECTS
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/david_koll Dr. David Koll]
|ta=None
|time=TBA
|place=TBA
|univz=TBA
}}

==Course Organization==
In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets.

While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:

* Introduction to the practical machine learning pipeline
* Exploratory data analysis
* The Python Data Science stack
* How to deal with unbalanced data
* Advanced algorithms for Data Science (an overview of competition winning algorithms)
* Parameter tuning for predictive models

Students need to submit their solutions to tasks by specific deadlines throughout the course. Note that this course thus requires a continous effort throughout the whole semester.
Solutions for each task have to be presented in class. A final report needs to be submitted at the end of the semester (March 31).

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Coursera Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.).
*Knowledge of Python or R...
*...and relevant libraries (e.g., SciKit Learn, Pandas, caret, etc.)

==Preliminary Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''When?'''
|{{Hl2}} |'''What?'''
|{{Hl2}} |'''Materials'''
|-
| align="right" | 19.10.2017
| Lecture 1: Introduction & The Data Science Pipeline
|
|-
| align="right" | 26.10.2017
| Lecture 2: The Python Data Science Stack
|
|-
| align="right" | 02.11.2017
| Task 1: Intermediate meeting
|
|-
| align="right" | 09.11.2017
| No lecture
|
|-
| align="right" | 16.11.2017
| No lecture
|
|-
| align="right" | 23.11.2017
| Task 1: Presentations // Task 2: Release
|
|-
| align="right" | 30.11.2017
| Lecture 3: Advanced algorithms for Data Science
|
|-
| align="right" | 07.12.2017
| Task 2: Intermediate meeting
|
|-
| align="right" | 14.12.2017
| Lecture 4: Evaluation and Tuning of Models
|
|-
| align="right" | 21.12.2017
| Task 2: Presentations // Task 3: Release
|
|-
| align="right" | 04.01.2018
| No lecture
|
|-
| align="right" | 11.01.2018
| Task 3: Intermediate meeting I
|
|-
| align="right" | 18.01.2018
| No lecture
|
|-
| align="right" | 25.01.2018
| Task 3: Intermediate meeting II
|
|-
| align="right" | 01.02.2018
| No lecture
|
|-
| align="right" | 08.02.2018-22.02.2018
| Task 3: Presentations
|
|-
| align="right" | 31.03.2018
| Final Report deadline
|
|-
|}

Advanced Practical Course Data Science for Computer Networks (Winter 2017/2018)

2017-07-27T09:23:52Z

Dkoll: /* Preliminary Schedule */

== Details ==
{{CourseDetails
|credits=180h, 6 ECTS
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/david_koll Dr. David Koll]
|ta=None
|time=TBA
|place=TBA
|univz=TBA
}}

==Course Organization==
In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets.

While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:

* Introduction to the practical machine learning pipeline
* Exploratory data analysis
* The Python Data Science stack
* Advanced algorithms for Data Science (an overview of competition winning algorithms)
* Parameter tuning for predictive models

Students need to submit their solutions to tasks by specific deadlines throughout the course. Note that this course thus requires a continous effort throughout the whole semester.
Solutions for each task have to be presented in class. A final report needs to be submitted at the end of the semester (March 31).

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Coursera Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.).
*Knowledge of Python or R...
*...and relevant libraries (e.g., SciKit Learn, Pandas, caret, etc.)

==Preliminary Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''When?'''
|{{Hl2}} |'''What?'''
|{{Hl2}} |'''Materials'''
|-
| align="right" | 19.10.2017
| Lecture 1: Introduction & The Data Science Pipeline
|
|-
| align="right" | 26.10.2017
| Lecture 2: The Python Data Science Stack
|
|-
| align="right" | 02.11.2017
| Task 1: Intermediate meeting
|
|-
| align="right" | 09.11.2017
| No lecture
|
|-
| align="right" | 16.11.2017
| No lecture
|
|-
| align="right" | 23.11.2017
| Task 1: Presentations // Task 2: Release
|
|-
| align="right" | 30.11.2017
| Lecture 3: Advanced algorithms for Data Science
|
|-
| align="right" | 07.12.2017
| Task 2: Intermediate meeting
|
|-
| align="right" | 14.12.2017
| Lecture 4: Evaluation and Tuning of Models
|
|-
| align="right" | 21.12.2017
| Task 2: Presentations // Task 3: Release
|
|-
| align="right" | 04.01.2018
| No lecture
|
|-
| align="right" | 11.01.2018
| Task 3: Intermediate meeting I
|
|-
| align="right" | 18.01.2018
| No lecture
|
|-
| align="right" | 25.01.2018
| Task 3: Intermediate meeting II
|
|-
| align="right" | 01.02.2018
| No lecture
|
|-
| align="right" | 08.02.2018-22.02.2018
| Task 3: Presentations
|
|-
| align="right" | 31.03.2018
| Final Report deadline
|
|-
|}

Advanced Practical Course Data Science for Computer Networks (Winter 2017/2018)

2017-07-27T09:23:42Z

Dkoll: /* Preliminary Schedule */

== Details ==
{{CourseDetails
|credits=180h, 6 ECTS
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/david_koll Dr. David Koll]
|ta=None
|time=TBA
|place=TBA
|univz=TBA
}}

==Course Organization==
In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets.

While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:

* Introduction to the practical machine learning pipeline
* Exploratory data analysis
* The Python Data Science stack
* Advanced algorithms for Data Science (an overview of competition winning algorithms)
* Parameter tuning for predictive models

Students need to submit their solutions to tasks by specific deadlines throughout the course. Note that this course thus requires a continous effort throughout the whole semester.
Solutions for each task have to be presented in class. A final report needs to be submitted at the end of the semester (March 31).

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Coursera Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.).
*Knowledge of Python or R...
*...and relevant libraries (e.g., SciKit Learn, Pandas, caret, etc.)

==Preliminary Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''When?'''
|{{Hl2}} |'''What?'''
|{{Hl2}} |'''Materials'''
|-
| align="right" | 19.10.2017
| Lecture 1: Introduction & The Data Science Pipeline
|
|-
| align="right" | 26.10.2017
| Lecture 2: The Python Data Science Stack
|
|-
| align="right" | 02.11.2017
| Task 1: Intermediate meeting
|
|-
| align="right" | 09.11.2017
| No lecture
|[[Media:CN_WS20162017_4.pdf | pdf]]
|-
| align="right" | 16.11.2017
| No lecture
|
|-
| align="right" | 23.11.2017
| Task 1: Presentations // Task 2: Release
|
|-
| align="right" | 30.11.2017
| Lecture 3: Advanced algorithms for Data Science
|
|-
| align="right" | 07.12.2017
| Task 2: Intermediate meeting
|
|-
| align="right" | 14.12.2017
| Lecture 4: Evaluation and Tuning of Models
|
|-
| align="right" | 21.12.2017
| Task 2: Presentations // Task 3: Release
|
|-
| align="right" | 04.01.2018
| No lecture
|
|-
| align="right" | 11.01.2018
| Task 3: Intermediate meeting I
|
|-
| align="right" | 18.01.2018
| No lecture
|
|-
| align="right" | 25.01.2018
| Task 3: Intermediate meeting II
|
|-
| align="right" | 01.02.2018
| No lecture
|
|-
| align="right" | 08.02.2018-22.02.2018
| Task 3: Presentations
|
|-
| align="right" | 31.03.2018
| Final Report deadline
|
|-
|}

Advanced Practical Course Data Science for Computer Networks (Winter 2017/2018)

2017-07-27T09:23:33Z

Dkoll:

== Details ==
{{CourseDetails
|credits=180h, 6 ECTS
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/david_koll Dr. David Koll]
|ta=None
|time=TBA
|place=TBA
|univz=TBA
}}

==Course Organization==
In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets.

While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:

* Introduction to the practical machine learning pipeline
* Exploratory data analysis
* The Python Data Science stack
* Advanced algorithms for Data Science (an overview of competition winning algorithms)
* Parameter tuning for predictive models

Students need to submit their solutions to tasks by specific deadlines throughout the course. Note that this course thus requires a continous effort throughout the whole semester.
Solutions for each task have to be presented in class. A final report needs to be submitted at the end of the semester (March 31).

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Coursera Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.).
*Knowledge of Python or R...
*...and relevant libraries (e.g., SciKit Learn, Pandas, caret, etc.)

==Preliminary Schedule==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''When?'''
|{{Hl2}} |'''What?'''
|{{Hl2}} |'''Materials'''
|-
| align="right" | 19.10.2017
| Lecture 1: Introduction & The Data Science Pipeline
| [[Media:CN_WS20162017_1.pdf | pdf]]
|-
| align="right" | 26.10.2017
| Lecture 2: The Python Data Science Stack
|
|-
| align="right" | 02.11.2017
| Task 1: Intermediate meeting
|
|-
| align="right" | 09.11.2017
| No lecture
|[[Media:CN_WS20162017_4.pdf | pdf]]
|-
| align="right" | 16.11.2017
| No lecture
|
|-
| align="right" | 23.11.2017
| Task 1: Presentations // Task 2: Release
|
|-
| align="right" | 30.11.2017
| Lecture 3: Advanced algorithms for Data Science
|
|-
| align="right" | 07.12.2017
| Task 2: Intermediate meeting
|
|-
| align="right" | 14.12.2017
| Lecture 4: Evaluation and Tuning of Models
|
|-
| align="right" | 21.12.2017
| Task 2: Presentations // Task 3: Release
|
|-
| align="right" | 04.01.2018
| No lecture
|
|-
| align="right" | 11.01.2018
| Task 3: Intermediate meeting I
|
|-
| align="right" | 18.01.2018
| No lecture
|
|-
| align="right" | 25.01.2018
| Task 3: Intermediate meeting II
|
|-
| align="right" | 01.02.2018
| No lecture
|
|-
| align="right" | 08.02.2018-22.02.2018
| Task 3: Presentations
|
|-
| align="right" | 31.03.2018
| Final Report deadline
|
|-
|}

Advanced Practical Course Data Science for Computer Networks (Winter 2017/2018)

2017-07-27T09:13:31Z

Dkoll: /* Course Organization */

== Details ==
{{CourseDetails
|credits=180h, 6 ECTS
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/david_koll Dr. David Koll]
|ta=None
|time=TBA
|place=TBA
|univz=TBA
}}

==Course Organization==
In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets.

While the focus of the course is strongly practical, to support students, the course will provide lectures on different aspects of practical machine learning in the early stages of the course, including:

* Introduction to the practical machine learning pipeline
* Exploratory data analysis
* The Python Data Science stack
* Advanced algorithms for Data Science (an overview of competition winning algorithms)
* Parameter tuning for predictive models

Students need to submit their solutions to tasks by specific deadlines throughout the course. Note that this course thus requires a continous effort throughout the whole semester.
Solutions for each task have to be presented in class. A final report needs to be submitted at the end of the semester (March 31).

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Coursera Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.).
*Knowledge of Python or R...
*...and relevant libraries (e.g., SciKit Learn, Pandas, caret, etc.)

Teaching

2017-07-24T17:11:14Z

Dkoll: /* Courses Winter Semester 2017/2018 */

== Courses Winter Semester 2017/2018 ==
Note: We will update the respective pages soon.
* [[Advanced Practical Course Data Science for Computer Networks (Winter 2017/2018) | Practical Course: Data Science]] (MSc) (PhD/BSc welcome)
* [[Practical Course Networking Lab (Winter 2017/2018) | Practical Course Networking Lab ]] (BSc)
* [[Software-defined Networking (Winter 2017/2018) | Block Course: Software-defined Networking]] (MSc) (''Course period: 9 October 2017 (Mon) - 13 Oct 2017 (Fri)'') (NOTE: The course structure will be different to past years)

== Courses Summer Semester 2017 ==
* [[Advanced Practical Course Data Science for Computer Networks (Summer 2017) | Advanced Practical Course: Data Science for Computer Networks ]] (MSc) (BSc welcome)
* [[Seminar on Internet Technologies (Summer 2017) | Seminar on Internet Technologies (Summer 2017) ]] (MSc)
* [[Advanced Computer Networks (Summer 2017) | Advanced Computer Networks ]] (MSc)
* [[Practical Course Networking Lab (Summer 2017) | Practical Course Networking Lab ]] (BSc)
* [[Computer Networks (Summer 2017) | Computer Networks (Exam only!)]] (BSc)

== Courses Winter Semester 2016/2017 ==
Note: We will update the respective pages soon.
* [[Practical Course Networking Lab (Winter 2016/2017) | Practical Course Networking Lab]] (BSc)
* [[Computer Networks (Winter 2016/2017) | Computer Networks]] (BSc)
* [[Practical Course on Data Science for Computer Networks (Winter 2016/2017) | Practical Course on Data Science for Computer Networks]] (MSc)
* [[Software-defined Networking (Winder 2016/2017) | Block Course: Software-defined Networking]] (MSc) (''Course period: 22 Feb 2017 (wed) - 2 Mar 2017 (Thu)'')
* [[Seminar on Internet Technologies (Winter 2016/2017) | Seminar on Internet Technologies]] (BSc/MSc)

== Courses Summer Semester 2016 ==
* [[Practical Course Networking Lab (Summer 2016) | Practical Course Networking Lab ]] (BSc)
* [[Practical Course Advanced Networking (Summer 2016) | Practical Course Advanced Networking: Data Science Edition]] (MSc)
* [[Advanced Topics in Mobile Communications (AToMIC): Social Network in Mobile Big Data (Summer 2016)]] (MSc)
* [[Seminar on Internet Technologies (Summer 2016) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2016) | Computer Networks (Exam only!)]] (BSc)
* [[Advanced Computer Networks (Summer 2016) | Advanced Computer Networks]] (MSc)

== Courses Winter Semester 2015/2016 ==

* [[Advanced Topics in Computer Networking (Winter 2015/2016) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2015/2016) | Practical Course Networking Lab]] (BSc)
* [[Seminar on Internet Technologies (Winter 2015/2016) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2015/2016) | Computer Networks]] (BSc)
Block courses:
* [[Introduction to Software-defined Networking (Winter 2015/2016) | Introduction to Software-defined Networking]] (MSc) (14-18 March 2016)
* [[Specialization Software-defined Networking (Winter 2015/2016) | Specialization Software-defined Networking]] (MSc) (21-25 March 2016)

== Courses Summer Semester 2015 ==
* [[Practical Course Networking Lab (Summer 2015) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Summer 2015) | Practical Course Advanced Networking]] (MSc)
* [[Advanced Topics in Mobile Communications (AToMIC)]] (MSc)
* [[Seminar on Internet Technologies (Summer 2015) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2015) | Computer Networks (Exam only!)]] (BSc)
* [[Advanced Computer Networks (Summer 2015) | Advanced Computer Networks]] (MSc)
* [[Machine Learning and Pervasive Computing (Summer 2015) | Machine Learning and Pervasive Computing]] (MSc)

== Courses Winter Semester 2014/2015 ==
* [[Advanced Topics in Computer Networking (Winter 2014/2015) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2014/2015) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Winter 2014/2015) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Winter 2014/2015) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2014/2015) | Computer Networks]] (BSc)
* [[Machine Learning and Pervasive Computing (Winter 2014/2015) | Machine Learning and Pervasive Computing]] (MSc)
* [[Introduction to Software-defined Networking (Winter 2014/2015) | Introduction to Software-defined Networking]] (MSc)
* [[Specialization Software-defined Networking (Winter 2014/2015) | Specialization Software-defined Networking]] (MSc)

== Courses Summer Semester 2014 ==
* [[Advanced Topics in Social Network and Big Data Methods(Summer 2014) | Advanced Topics in Social Network and Big Data Methods ]] (MSc)
* [[Advances in Mobile Applications and Mobile Cloud Computing(Summer 2014) | Advances in Mobile Applications and Mobile Cloud Computing ]] (MSc)
* [[Practical Course Networking Lab (Summer 2014) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Summer 2014) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Summer 2014) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2014) | Computer Networks (Exam only!)]] (BSc)
* [[Advanced Computer Networks (Summer 2014) | Advanced Computer Networks]] (MSc)

== Courses Winter Semester 2013/14 ==
* [[Advanced Topics in Computer Networking (Winter 2013/2014) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2013/2014) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Winter 2013/2014) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Winter 2013/2014) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2013/2014) | Computer Networks]] (BSc)
* [[Selected topics in Pervasive Computing (Winter 2013/2014) | Selected Topics in Pervasive Computing]] (MSc)

== Courses Summer Semester 2013 ==
* [[Advanced Topics in Mobile Communications (Summer 2013) | Advanced Topics in Mobile Communications (AToMIC)]] (MSc)
* [[Practical Course Networking Lab (Summer 2013) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Summer 2013) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Summer 2013) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2013) | Computer Networks]] (BSc)
* [[Advanced Computer Networks (Summer 2013) | Advanced Computer Networks]] (MSc)

== Courses Winter Semester 2012/13 ==
* [[Advanced Topics in Computer Networking (Winter 2012/2013) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2012/2013) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Winter 2012/2013) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Winter 2012/2013) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2012/2013) | Computer Networks]] (BSc)
* [http://www.swe.informatik.uni-goettingen.de/lectures/social-networks-seminar-ws2012 Social Networks Seminar] (MSc)

== Courses Summer Semester 2012 ==
* [[Advanced Topics in Mobile Communications (Summer 2012) | Advanced Topics in Mobile Communications (AToMIC)]] (MSc)
* [[Practical Course Networking Lab (Summer 2012) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Summer 2012) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Summer 2012) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2012) | Computer Networks]] (BSc)
* [[Advanced Computer Networks (Summer 2012) | Advanced Computer Networks]] (MSc)

== Courses Winter Semester 2011/2012 ==
* [[Advanced Topics in Computer Networking (Winter 2011/2012) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2011/2012) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Winter 2011/2012) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Winter 2011/2012) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2011/2012) | Computer Networks]] (BSc)
* [[Social Networks Colloquium (Winter 2011/2012) | Social Networks Colloquium]] (BSc/MSc)

== Courses Summer Semester 2011 ==
* [[Advanced Topics in Mobile Communications (Summer 2011) | Advanced Topics in Mobile Communications (AToMIC)]] (MSc)
* [[Practical Course Networking Lab (Summer 2011) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Summer 2011) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Summer 2011) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2011) | Computer Networks]] (BSc)

== Courses Winter Semester 2010/2011 ==
* [[Advanced Topics in Computer Networking (Winter 2010/2011) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2010/2011) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Winter 2010/2011) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Winter 2010/2011) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2010/2011) | Computer Networks (previously Telematik)]] (BSc)
* [[Seminar on Mathematical Models in Computer Networks (Winter 2010/2011) | Seminar on Mathematical Models]] (MSc/PhD)

== Courses Summer Semester 2010 ==
* [[Advanced Topics in Mobile Communications (Summer 2010) | Advanced Topics in Mobile Communications (AToMIC)]] (MSc)
* [[Practical Course Networking Lab (Summer 2010) | Practical Course Networking Lab]] (BSc)
* [[Seminar on Internet Technologies (Summer 2010) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Telematics (Summer 2010) | Telematik/Telematics (Exam only)]] (BSc)

<noinclude>

== Courses Winter Semester 2009/2010 ==
* [[Advanced Topics in Computer Networking (Winter 2009/2010) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2009/2010) | Practical Course Networking Lab]] (BSc)
* [[Seminar on Internet Technologies (Winter 2009/2010) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Telematik (Winter 2009/2010) | Telematik]] (BSc)

<noinclude>

== Courses Summer Semester 2009 ==
* [http://www.net.informatik.uni-goettingen.de/teaching/1595 Advanced Topics in Mobile Communications (AToMIC)]
* [[Practical Course Networking Lab (Summer 2009) | Practical Course Networking Lab]]
* [[Seminar on Internet Technologies (Summer 2009) | Seminar on Internet Technologies]]
* [http://www.net.informatik.uni-goettingen.de/teaching/1599 Telematik Exam]

== Courses before Summer 2009==
* For a list of older courses please go [http://www.net.informatik.uni-goettingen.de/teaching here].
</noinclude>

Teaching

2017-07-24T17:10:58Z

Dkoll: /* Courses Winter Semester 2017/2018 */

== Courses Winter Semester 2017/2018 ==
Note: We will update the respective pages soon.
* [[Practical Course Data Science for Computer Networks (Winter 2017/2018) | Advanced Practical Course: Data Science]] (MSc) (PhD/BSc welcome)
* [[Practical Course Networking Lab (Winter 2017/2018) | Practical Course Networking Lab ]] (BSc)
* [[Software-defined Networking (Winter 2017/2018) | Block Course: Software-defined Networking]] (MSc) (''Course period: 9 October 2017 (Mon) - 13 Oct 2017 (Fri)'') (NOTE: The course structure will be different to past years)

== Courses Summer Semester 2017 ==
* [[Advanced Practical Course Data Science for Computer Networks (Summer 2017) | Advanced Practical Course: Data Science for Computer Networks ]] (MSc) (BSc welcome)
* [[Seminar on Internet Technologies (Summer 2017) | Seminar on Internet Technologies (Summer 2017) ]] (MSc)
* [[Advanced Computer Networks (Summer 2017) | Advanced Computer Networks ]] (MSc)
* [[Practical Course Networking Lab (Summer 2017) | Practical Course Networking Lab ]] (BSc)
* [[Computer Networks (Summer 2017) | Computer Networks (Exam only!)]] (BSc)

== Courses Winter Semester 2016/2017 ==
Note: We will update the respective pages soon.
* [[Practical Course Networking Lab (Winter 2016/2017) | Practical Course Networking Lab]] (BSc)
* [[Computer Networks (Winter 2016/2017) | Computer Networks]] (BSc)
* [[Practical Course on Data Science for Computer Networks (Winter 2016/2017) | Practical Course on Data Science for Computer Networks]] (MSc)
* [[Software-defined Networking (Winder 2016/2017) | Block Course: Software-defined Networking]] (MSc) (''Course period: 22 Feb 2017 (wed) - 2 Mar 2017 (Thu)'')
* [[Seminar on Internet Technologies (Winter 2016/2017) | Seminar on Internet Technologies]] (BSc/MSc)

== Courses Summer Semester 2016 ==
* [[Practical Course Networking Lab (Summer 2016) | Practical Course Networking Lab ]] (BSc)
* [[Practical Course Advanced Networking (Summer 2016) | Practical Course Advanced Networking: Data Science Edition]] (MSc)
* [[Advanced Topics in Mobile Communications (AToMIC): Social Network in Mobile Big Data (Summer 2016)]] (MSc)
* [[Seminar on Internet Technologies (Summer 2016) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2016) | Computer Networks (Exam only!)]] (BSc)
* [[Advanced Computer Networks (Summer 2016) | Advanced Computer Networks]] (MSc)

== Courses Winter Semester 2015/2016 ==

* [[Advanced Topics in Computer Networking (Winter 2015/2016) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2015/2016) | Practical Course Networking Lab]] (BSc)
* [[Seminar on Internet Technologies (Winter 2015/2016) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2015/2016) | Computer Networks]] (BSc)
Block courses:
* [[Introduction to Software-defined Networking (Winter 2015/2016) | Introduction to Software-defined Networking]] (MSc) (14-18 March 2016)
* [[Specialization Software-defined Networking (Winter 2015/2016) | Specialization Software-defined Networking]] (MSc) (21-25 March 2016)

== Courses Summer Semester 2015 ==
* [[Practical Course Networking Lab (Summer 2015) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Summer 2015) | Practical Course Advanced Networking]] (MSc)
* [[Advanced Topics in Mobile Communications (AToMIC)]] (MSc)
* [[Seminar on Internet Technologies (Summer 2015) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2015) | Computer Networks (Exam only!)]] (BSc)
* [[Advanced Computer Networks (Summer 2015) | Advanced Computer Networks]] (MSc)
* [[Machine Learning and Pervasive Computing (Summer 2015) | Machine Learning and Pervasive Computing]] (MSc)

== Courses Winter Semester 2014/2015 ==
* [[Advanced Topics in Computer Networking (Winter 2014/2015) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2014/2015) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Winter 2014/2015) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Winter 2014/2015) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2014/2015) | Computer Networks]] (BSc)
* [[Machine Learning and Pervasive Computing (Winter 2014/2015) | Machine Learning and Pervasive Computing]] (MSc)
* [[Introduction to Software-defined Networking (Winter 2014/2015) | Introduction to Software-defined Networking]] (MSc)
* [[Specialization Software-defined Networking (Winter 2014/2015) | Specialization Software-defined Networking]] (MSc)

== Courses Summer Semester 2014 ==
* [[Advanced Topics in Social Network and Big Data Methods(Summer 2014) | Advanced Topics in Social Network and Big Data Methods ]] (MSc)
* [[Advances in Mobile Applications and Mobile Cloud Computing(Summer 2014) | Advances in Mobile Applications and Mobile Cloud Computing ]] (MSc)
* [[Practical Course Networking Lab (Summer 2014) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Summer 2014) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Summer 2014) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2014) | Computer Networks (Exam only!)]] (BSc)
* [[Advanced Computer Networks (Summer 2014) | Advanced Computer Networks]] (MSc)

== Courses Winter Semester 2013/14 ==
* [[Advanced Topics in Computer Networking (Winter 2013/2014) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2013/2014) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Winter 2013/2014) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Winter 2013/2014) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2013/2014) | Computer Networks]] (BSc)
* [[Selected topics in Pervasive Computing (Winter 2013/2014) | Selected Topics in Pervasive Computing]] (MSc)

== Courses Summer Semester 2013 ==
* [[Advanced Topics in Mobile Communications (Summer 2013) | Advanced Topics in Mobile Communications (AToMIC)]] (MSc)
* [[Practical Course Networking Lab (Summer 2013) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Summer 2013) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Summer 2013) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2013) | Computer Networks]] (BSc)
* [[Advanced Computer Networks (Summer 2013) | Advanced Computer Networks]] (MSc)

== Courses Winter Semester 2012/13 ==
* [[Advanced Topics in Computer Networking (Winter 2012/2013) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2012/2013) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Winter 2012/2013) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Winter 2012/2013) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2012/2013) | Computer Networks]] (BSc)
* [http://www.swe.informatik.uni-goettingen.de/lectures/social-networks-seminar-ws2012 Social Networks Seminar] (MSc)

== Courses Summer Semester 2012 ==
* [[Advanced Topics in Mobile Communications (Summer 2012) | Advanced Topics in Mobile Communications (AToMIC)]] (MSc)
* [[Practical Course Networking Lab (Summer 2012) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Summer 2012) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Summer 2012) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2012) | Computer Networks]] (BSc)
* [[Advanced Computer Networks (Summer 2012) | Advanced Computer Networks]] (MSc)

== Courses Winter Semester 2011/2012 ==
* [[Advanced Topics in Computer Networking (Winter 2011/2012) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2011/2012) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Winter 2011/2012) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Winter 2011/2012) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2011/2012) | Computer Networks]] (BSc)
* [[Social Networks Colloquium (Winter 2011/2012) | Social Networks Colloquium]] (BSc/MSc)

== Courses Summer Semester 2011 ==
* [[Advanced Topics in Mobile Communications (Summer 2011) | Advanced Topics in Mobile Communications (AToMIC)]] (MSc)
* [[Practical Course Networking Lab (Summer 2011) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Summer 2011) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Summer 2011) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2011) | Computer Networks]] (BSc)

== Courses Winter Semester 2010/2011 ==
* [[Advanced Topics in Computer Networking (Winter 2010/2011) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2010/2011) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Winter 2010/2011) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Winter 2010/2011) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2010/2011) | Computer Networks (previously Telematik)]] (BSc)
* [[Seminar on Mathematical Models in Computer Networks (Winter 2010/2011) | Seminar on Mathematical Models]] (MSc/PhD)

== Courses Summer Semester 2010 ==
* [[Advanced Topics in Mobile Communications (Summer 2010) | Advanced Topics in Mobile Communications (AToMIC)]] (MSc)
* [[Practical Course Networking Lab (Summer 2010) | Practical Course Networking Lab]] (BSc)
* [[Seminar on Internet Technologies (Summer 2010) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Telematics (Summer 2010) | Telematik/Telematics (Exam only)]] (BSc)

<noinclude>

== Courses Winter Semester 2009/2010 ==
* [[Advanced Topics in Computer Networking (Winter 2009/2010) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2009/2010) | Practical Course Networking Lab]] (BSc)
* [[Seminar on Internet Technologies (Winter 2009/2010) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Telematik (Winter 2009/2010) | Telematik]] (BSc)

<noinclude>

== Courses Summer Semester 2009 ==
* [http://www.net.informatik.uni-goettingen.de/teaching/1595 Advanced Topics in Mobile Communications (AToMIC)]
* [[Practical Course Networking Lab (Summer 2009) | Practical Course Networking Lab]]
* [[Seminar on Internet Technologies (Summer 2009) | Seminar on Internet Technologies]]
* [http://www.net.informatik.uni-goettingen.de/teaching/1599 Telematik Exam]

== Courses before Summer 2009==
* For a list of older courses please go [http://www.net.informatik.uni-goettingen.de/teaching here].
</noinclude>

Advanced Practical Course Data Science for Computer Networks (Winter 2017/2018)

2017-07-24T13:13:09Z

Dkoll: /* Course Organization */

== Details ==
{{CourseDetails
|credits=180h, 6 ECTS
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/david_koll Dr. David Koll]
|ta=None
|time=TBA
|place=TBA
|univz=TBA
}}

==Course Organization==
In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets. The course is structured as a competition, i.e., all groups of students will receive the same tasks.

Each team will need to present their solution for each task. Intermediate reports will have to be submitted from time to time and a final report needs to be submitted at the end of the semester (March 31).

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Coursera Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.).
*Knowledge of Python or R...
*...and relevant libraries (e.g., SciKit Learn, Pandas, caret, etc.)

Teaching

2017-07-24T13:11:43Z

Dkoll: /* Courses Winter Semester 2017/2018 */

== Courses Winter Semester 2017/2018 ==
Note: We will update the respective pages soon.
* [[Advanced Practical Course Data Science for Computer Networks (Winter 2017/2018) | Advanced Practical Course: Data Science]] (MSc) (PhD/BSc welcome)
* [[Practical Course Networking Lab (Winter 2017/2018) | Practical Course Networking Lab ]] (BSc)
* [[Software-defined Networking (Winter 2017/2018) | Block Course: Software-defined Networking]] (MSc) (''Course period: 9 October 2017 (Mon) - 13 Oct 2017 (Fri)'') (NOTE: The course structure will be different to past years)

== Courses Summer Semester 2017 ==
* [[Advanced Practical Course Data Science for Computer Networks (Summer 2017) | Advanced Practical Course: Data Science for Computer Networks ]] (MSc) (BSc welcome)
* [[Seminar on Internet Technologies (Summer 2017) | Seminar on Internet Technologies (Summer 2017) ]] (MSc)
* [[Advanced Computer Networks (Summer 2017) | Advanced Computer Networks ]] (MSc)
* [[Practical Course Networking Lab (Summer 2017) | Practical Course Networking Lab ]] (BSc)
* [[Computer Networks (Summer 2017) | Computer Networks (Exam only!)]] (BSc)

== Courses Winter Semester 2016/2017 ==
Note: We will update the respective pages soon.
* [[Practical Course Networking Lab (Winter 2016/2017) | Practical Course Networking Lab]] (BSc)
* [[Computer Networks (Winter 2016/2017) | Computer Networks]] (BSc)
* [[Practical Course on Data Science for Computer Networks (Winter 2016/2017) | Practical Course on Data Science for Computer Networks]] (MSc)
* [[Software-defined Networking (Winder 2016/2017) | Block Course: Software-defined Networking]] (MSc) (''Course period: 22 Feb 2017 (wed) - 2 Mar 2017 (Thu)'')
* [[Seminar on Internet Technologies (Winter 2016/2017) | Seminar on Internet Technologies]] (BSc/MSc)

== Courses Summer Semester 2016 ==
* [[Practical Course Networking Lab (Summer 2016) | Practical Course Networking Lab ]] (BSc)
* [[Practical Course Advanced Networking (Summer 2016) | Practical Course Advanced Networking: Data Science Edition]] (MSc)
* [[Advanced Topics in Mobile Communications (AToMIC): Social Network in Mobile Big Data (Summer 2016)]] (MSc)
* [[Seminar on Internet Technologies (Summer 2016) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2016) | Computer Networks (Exam only!)]] (BSc)
* [[Advanced Computer Networks (Summer 2016) | Advanced Computer Networks]] (MSc)

== Courses Winter Semester 2015/2016 ==

* [[Advanced Topics in Computer Networking (Winter 2015/2016) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2015/2016) | Practical Course Networking Lab]] (BSc)
* [[Seminar on Internet Technologies (Winter 2015/2016) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2015/2016) | Computer Networks]] (BSc)
Block courses:
* [[Introduction to Software-defined Networking (Winter 2015/2016) | Introduction to Software-defined Networking]] (MSc) (14-18 March 2016)
* [[Specialization Software-defined Networking (Winter 2015/2016) | Specialization Software-defined Networking]] (MSc) (21-25 March 2016)

== Courses Summer Semester 2015 ==
* [[Practical Course Networking Lab (Summer 2015) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Summer 2015) | Practical Course Advanced Networking]] (MSc)
* [[Advanced Topics in Mobile Communications (AToMIC)]] (MSc)
* [[Seminar on Internet Technologies (Summer 2015) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2015) | Computer Networks (Exam only!)]] (BSc)
* [[Advanced Computer Networks (Summer 2015) | Advanced Computer Networks]] (MSc)
* [[Machine Learning and Pervasive Computing (Summer 2015) | Machine Learning and Pervasive Computing]] (MSc)

== Courses Winter Semester 2014/2015 ==
* [[Advanced Topics in Computer Networking (Winter 2014/2015) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2014/2015) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Winter 2014/2015) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Winter 2014/2015) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2014/2015) | Computer Networks]] (BSc)
* [[Machine Learning and Pervasive Computing (Winter 2014/2015) | Machine Learning and Pervasive Computing]] (MSc)
* [[Introduction to Software-defined Networking (Winter 2014/2015) | Introduction to Software-defined Networking]] (MSc)
* [[Specialization Software-defined Networking (Winter 2014/2015) | Specialization Software-defined Networking]] (MSc)

== Courses Summer Semester 2014 ==
* [[Advanced Topics in Social Network and Big Data Methods(Summer 2014) | Advanced Topics in Social Network and Big Data Methods ]] (MSc)
* [[Advances in Mobile Applications and Mobile Cloud Computing(Summer 2014) | Advances in Mobile Applications and Mobile Cloud Computing ]] (MSc)
* [[Practical Course Networking Lab (Summer 2014) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Summer 2014) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Summer 2014) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2014) | Computer Networks (Exam only!)]] (BSc)
* [[Advanced Computer Networks (Summer 2014) | Advanced Computer Networks]] (MSc)

== Courses Winter Semester 2013/14 ==
* [[Advanced Topics in Computer Networking (Winter 2013/2014) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2013/2014) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Winter 2013/2014) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Winter 2013/2014) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2013/2014) | Computer Networks]] (BSc)
* [[Selected topics in Pervasive Computing (Winter 2013/2014) | Selected Topics in Pervasive Computing]] (MSc)

== Courses Summer Semester 2013 ==
* [[Advanced Topics in Mobile Communications (Summer 2013) | Advanced Topics in Mobile Communications (AToMIC)]] (MSc)
* [[Practical Course Networking Lab (Summer 2013) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Summer 2013) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Summer 2013) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2013) | Computer Networks]] (BSc)
* [[Advanced Computer Networks (Summer 2013) | Advanced Computer Networks]] (MSc)

== Courses Winter Semester 2012/13 ==
* [[Advanced Topics in Computer Networking (Winter 2012/2013) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2012/2013) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Winter 2012/2013) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Winter 2012/2013) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2012/2013) | Computer Networks]] (BSc)
* [http://www.swe.informatik.uni-goettingen.de/lectures/social-networks-seminar-ws2012 Social Networks Seminar] (MSc)

== Courses Summer Semester 2012 ==
* [[Advanced Topics in Mobile Communications (Summer 2012) | Advanced Topics in Mobile Communications (AToMIC)]] (MSc)
* [[Practical Course Networking Lab (Summer 2012) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Summer 2012) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Summer 2012) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2012) | Computer Networks]] (BSc)
* [[Advanced Computer Networks (Summer 2012) | Advanced Computer Networks]] (MSc)

== Courses Winter Semester 2011/2012 ==
* [[Advanced Topics in Computer Networking (Winter 2011/2012) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2011/2012) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Winter 2011/2012) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Winter 2011/2012) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2011/2012) | Computer Networks]] (BSc)
* [[Social Networks Colloquium (Winter 2011/2012) | Social Networks Colloquium]] (BSc/MSc)

== Courses Summer Semester 2011 ==
* [[Advanced Topics in Mobile Communications (Summer 2011) | Advanced Topics in Mobile Communications (AToMIC)]] (MSc)
* [[Practical Course Networking Lab (Summer 2011) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Summer 2011) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Summer 2011) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Summer 2011) | Computer Networks]] (BSc)

== Courses Winter Semester 2010/2011 ==
* [[Advanced Topics in Computer Networking (Winter 2010/2011) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2010/2011) | Practical Course Networking Lab]] (BSc)
* [[Practical Course Advanced Networking (Winter 2010/2011) | Practical Course Advanced Networking]] (MSc)
* [[Seminar on Internet Technologies (Winter 2010/2011) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Computer Networks (Winter 2010/2011) | Computer Networks (previously Telematik)]] (BSc)
* [[Seminar on Mathematical Models in Computer Networks (Winter 2010/2011) | Seminar on Mathematical Models]] (MSc/PhD)

== Courses Summer Semester 2010 ==
* [[Advanced Topics in Mobile Communications (Summer 2010) | Advanced Topics in Mobile Communications (AToMIC)]] (MSc)
* [[Practical Course Networking Lab (Summer 2010) | Practical Course Networking Lab]] (BSc)
* [[Seminar on Internet Technologies (Summer 2010) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Telematics (Summer 2010) | Telematik/Telematics (Exam only)]] (BSc)

<noinclude>

== Courses Winter Semester 2009/2010 ==
* [[Advanced Topics in Computer Networking (Winter 2009/2010) | Advanced Topics in Computer Networking]] (MSc)
* [[Practical Course Networking Lab (Winter 2009/2010) | Practical Course Networking Lab]] (BSc)
* [[Seminar on Internet Technologies (Winter 2009/2010) | Seminar on Internet Technologies]] (BSc/MSc)
* [[Telematik (Winter 2009/2010) | Telematik]] (BSc)

<noinclude>

== Courses Summer Semester 2009 ==
* [http://www.net.informatik.uni-goettingen.de/teaching/1595 Advanced Topics in Mobile Communications (AToMIC)]
* [[Practical Course Networking Lab (Summer 2009) | Practical Course Networking Lab]]
* [[Seminar on Internet Technologies (Summer 2009) | Seminar on Internet Technologies]]
* [http://www.net.informatik.uni-goettingen.de/teaching/1599 Telematik Exam]

== Courses before Summer 2009==
* For a list of older courses please go [http://www.net.informatik.uni-goettingen.de/teaching here].
</noinclude>

Advanced Practical Course Data Science for Computer Networks (Winter 2017/2018)

2017-07-13T11:58:44Z

Dkoll: Created page with "== Details == {{CourseDetails |credits=180h, 6 ECTS |module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke |lecturer=[http://www.net.informatik.uni-goettingen.de/people..."

== Details ==
{{CourseDetails
|credits=180h, 6 ECTS
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/david_koll Dr. David Koll]
|ta=None
|time=TBA
|place=TBA
|univz=TBA
}}

==Course Organization==
In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets. The course is structured as a competition, i.e., all groups of students will receive the same tasks.

Each team will need to present their solution for each task. Intermediate reports will have to be submitted from time to time and a final report needs to be submitted at the end of the semester (September 30).

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Coursera Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.).
*Knowledge of Python or R...
*...and relevant libraries (e.g., SciKit Learn, Pandas, caret, etc.)

Advanced Practical Course Data Science for Computer Networks (Summer 2017)

2017-07-13T11:52:32Z

Dkoll:

{{Announcement | [https://listserv.gwdg.de/mailman/listinfo/ds Please subscribe to the course mailing list!]}}
{{Announcement | The fourth and final task is now online.}}

== Details ==
{{CourseDetails
|credits=180h, 6 ECTS
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/david_koll Dr. David Koll]
|ta=None
|time=Thursday, 14-16 (bi-weekly)
|place=IFI 3.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=196722&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
|
}}

==Course Organization==
In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets. The course is structured as a competition, i.e., all groups of students will receive the same tasks.

Each team will need to present their solution for each task. Intermediate reports will have to be submitted from time to time and a final report needs to be submitted at the end of the semester (September 30).

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Coursera Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.).
*Knowledge of Python or R...
*...and relevant libraries (e.g., SciKit Learn, Pandas, caret, etc.)

==Task Descriptions==
* [[Media:ds_task_1.pdf | Task 1: Bike Sharing]]
* [[Media:ds_ss_17_task_2.pdf | Task 2: Network Intrusion Detection]]
* [[Media:ds_ss_17_task_3.pdf | Task 3: Social Network Analysis]]
* Task 4: [[Media:ds_ss_17_task_4.pdf | Mobile Phone Data Analysis]] or Kaggle InstaCart Competition

==Schedule (Tentative)==
* April 13:
** Informational meeting
** Release of warmup problem
* April 20: Release of first project
* April 27: Submission of warmup problem (5% of final grade) in single PDF by E-Mail to David
** Submit as a PDF report
** In the PDF describe your steps in exploratory data analysis and link your results to the predictive model you have built.
** Also attach your Code
** Overall, this submission also decides on whether or not you will be able to continue the course.
* May 4th: Meeting to discuss properties of / problems with data set for first project
* May 18th:
**Presentation of first project results (20% of final grade)
** Release of second project
* MONDAY, June 12th, 16:00: Meeting to discuss properties of / problems with data set for second project
* TUESDAY, June 20 th, 16:00:
** Presentation of second project results (15% of final grade)
** Release of third project
* '''July 13, 16:00:''' Meeting to discuss properties of / problems with data set for third project
* August 3: Presentation of third project results (40% of final grade)
* September 30: Submission of final reports for projects 1-3 (20% of final grade)

All meetings will be at 2.15pm in room IFI 3.101.

Advanced Practical Course Data Science for Computer Networks (Summer 2017)

2017-07-13T11:42:21Z

Dkoll: /* Task Descriptions */

{{Announcement | [https://listserv.gwdg.de/mailman/listinfo/ds Please subscribe to the course mailing list!]}}
{{Announcement | The first task is now online.}}

== Details ==
{{CourseDetails
|credits=180h, 6 ECTS
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/david_koll Dr. David Koll]
|ta=None
|time=Thursday, 14-16 (bi-weekly)
|place=IFI 3.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=196722&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
|
}}

==Course Organization==
In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets. The course is structured as a competition, i.e., all groups of students will receive the same tasks.

Each team will need to present their solution for each task. Intermediate reports will have to be submitted from time to time and a final report needs to be submitted at the end of the semester (September 30).

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Coursera Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.).
*Knowledge of Python or R...
*...and relevant libraries (e.g., SciKit Learn, Pandas, caret, etc.)

==Task Descriptions==
* [[Media:ds_task_1.pdf | Task 1: Bike Sharing]]
* [[Media:ds_ss_17_task_2.pdf | Task 2: Network Intrusion Detection]]
* [[Media:ds_ss_17_task_3.pdf | Task 3: Social Network Analysis]]
* Task 4: [[Media:ds_ss_17_task_4.pdf | Mobile Phone Data Analysis]] or Kaggle InstaCart Competition

==Schedule (Tentative)==
* April 13:
** Informational meeting
** Release of warmup problem
* April 20: Release of first project
* April 27: Submission of warmup problem (5% of final grade) in single PDF by E-Mail to David
** Submit as a PDF report
** In the PDF describe your steps in exploratory data analysis and link your results to the predictive model you have built.
** Also attach your Code
** Overall, this submission also decides on whether or not you will be able to continue the course.
* May 4th: Meeting to discuss properties of / problems with data set for first project
* May 18th:
**Presentation of first project results (20% of final grade)
** Release of second project
* MONDAY, June 12th, 16:00: Meeting to discuss properties of / problems with data set for second project
* TUESDAY, June 20 th, 16:00:
** Presentation of second project results (15% of final grade)
** Release of third project
* '''July 13, 16:00:''' Meeting to discuss properties of / problems with data set for third project
* August 3: Presentation of third project results (40% of final grade)
* September 30: Submission of final reports for projects 1-3 (20% of final grade)

All meetings will be at 2.15pm in room IFI 3.101.

Advanced Practical Course Data Science for Computer Networks (Summer 2017)

2017-07-13T11:40:45Z

Dkoll: /* Schedule (Tentative) */

{{Announcement | [https://listserv.gwdg.de/mailman/listinfo/ds Please subscribe to the course mailing list!]}}
{{Announcement | The first task is now online.}}

== Details ==
{{CourseDetails
|credits=180h, 6 ECTS
|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/david_koll Dr. David Koll]
|ta=None
|time=Thursday, 14-16 (bi-weekly)
|place=IFI 3.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=196722&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
|
}}

==Course Organization==
In this course, you will complete several practical tasks in the realm of data analysis. These tasks can include both exploratory (descriptive) data analysis as well as the application of machine learning algorithms to specific datasets. The course is structured as a competition, i.e., all groups of students will receive the same tasks.

Each team will need to present their solution for each task. Intermediate reports will have to be submitted from time to time and a final report needs to be submitted at the end of the semester (September 30).

==Prerequisites==
*You are ''highly recommended'' to have completed a course on Data Science (e.g., "[https://www.swe.informatik.uni-goettingen.de/lectures/data-science-and-big-data-analytics-ws2015 Data Science and Big Data Analytics" taught by Dr. Steffen Herbold] or the Coursera Course "Machine Learning" by Stanford University) before entering this course. You need to be familiar with basic statistics (distributions, p/t/z-tests, etc.) and a range of machine learning algorithms (linear/logistic/lasso regression, k-means clustering, k-NN classification etc.).
*Knowledge of Python or R...
*...and relevant libraries (e.g., SciKit Learn, Pandas, caret, etc.)

==Task Descriptions==
* [[Media:ds_task_1.pdf | Task 1: Bike Sharing]]
* [[Media:ds_ss_17_task_2.pdf | Task 2: Network Intrusion Detection]]
* [[Media:ds_ss_17_task_3.pdf | Task 3: Social Network Analysis]]
* [[Media:ds_ss_17_task_4.pdf | Task 4: Mobile Phone Data Analysis]]

==Schedule (Tentative)==
* April 13:
** Informational meeting
** Release of warmup problem
* April 20: Release of first project
* April 27: Submission of warmup problem (5% of final grade) in single PDF by E-Mail to David
** Submit as a PDF report
** In the PDF describe your steps in exploratory data analysis and link your results to the predictive model you have built.
** Also attach your Code
** Overall, this submission also decides on whether or not you will be able to continue the course.
* May 4th: Meeting to discuss properties of / problems with data set for first project
* May 18th:
**Presentation of first project results (20% of final grade)
** Release of second project
* MONDAY, June 12th, 16:00: Meeting to discuss properties of / problems with data set for second project
* TUESDAY, June 20 th, 16:00:
** Presentation of second project results (15% of final grade)
** Release of third project
* '''July 13, 16:00:''' Meeting to discuss properties of / problems with data set for third project
* August 3: Presentation of third project results (40% of final grade)
* September 30: Submission of final reports for projects 1-3 (20% of final grade)

All meetings will be at 2.15pm in room IFI 3.101.

File:ACN SS2017 DCN.pdf

2017-07-10T05:55:24Z

Dkoll:

Advanced Computer Networks (Summer 2017)

2017-07-10T05:53:18Z

Dkoll: /* Schedule (Tentative) */

{{Announcement|Note: Due to a higher number of registrations, the room for the examination has been changed to GZG MN09 (building opposite of IFI, see link below for details). The time remains the same (the exam starts at 10.15, please be there at 10.00)}}

== Details ==
{{CourseDetails
|credits=5ECTS
|module= M.Inf.1223.Mp OR 3.17: Selected Topics in Advanced Networking (ITIS)
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/xiaoming_fu Prof. Xiaoming Fu], [http://www.net.informatik.uni-goettingen.de/people/mayutan_arumaithurai Dr. Mayutan Arumaithurai], [https://www.net.informatik.uni-goettingen.de/people/hong_huang Dr. Hong Huang], [http://user.informatik.uni-goettingen.de/~dkoll Dr. David Koll]
|ta=TBA
|time=Thursdays, 10-12am.
|place=IFI 3.101, EXAM: [https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&moduleCall=webInfo&publishConfFile=webInfoRaum&publishSubDir=raum&keep=y&raum.rgid=194&noDBAction=y&init=y MN09, GZG]
|univz=tba
}}

==Course description==
This lecture will introduce advanced concepts of computer networking to interested students. Topics include, but are not limited to:
*Mobile Edge Computing
*Social big data
*Cloud Computing
*Datacenter Networking
*Future Internet Technologies

For each topic, basic structures, features, applied techniques and security aspects will be taught.

==Schedule (Tentative)==
{| {{Prettytable|width=}}
|-
|{{Hl2}} |'''Date'''
|{{Hl2}} |'''Topic'''
|{{Hl2}} |'''Lecturer'''
|{{Hl2}} |'''Slides'''
|-
| align="right" | 13.04.2017
| Introduction
| Prof. X. Fu
| [https://wiki.net.informatik.uni-goettingen.de/w/images/b/bd/ACN_01_introduction.pdf]
|-
| align="right" | 20.04.2017
| Mobile Edge Computing
| Prof. X. Fu
| [https://wiki.net.informatik.uni-goettingen.de/w/images/d/de/MobileEdgeComputing.pdf]
|-
| align="right" | 27.04.2017
| NO LECTURE (Girls Day)
|
|
|-
| align="right" | 04.05.2017
| Crowdsourcing
| Prof. X. Fu
| [https://cis.temple.edu/~wu/research/publications/Publication_files/APDCM-2016.pdf]
|-
| align="right" | 11.05.2017
| Social Big Data - Introduction
| Dr. H. Huang
| [https://wiki.net.informatik.uni-goettingen.de/w/images/5/5c/Social_big_data_I.pdf]
|-
| align="right" | 18.05.2017
| Social Big Data - Methods
| Dr. H. Huang
| [https://wiki.net.informatik.uni-goettingen.de/wiki/File:Social_big_data2.pdf]
|-
| align="right" | 25.05.2017
| NO LECTURE (PUBLIC HOLIDAY)
|
|
|-
| align="right" | 01.06.2017
| Social Big Data - Applications
| Dr. H. Huang
| [https://wiki.net.informatik.uni-goettingen.de/wiki/File:Social_big_data3.pdf]
|-
| align="right" | 08.06.2017
| Information Centric Networks I
| Dr. M. Arumaithurai
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/ACN/2017_SS/0.CCN_I.pdf][https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/ACN/2017_SS/0.CCN_II.pdf]
|-
| align="right" | 15.06.2017
| Software-defined Networking I
| Dr. D. Koll
| [[Media:ACN_SS2017_SDN1.pdf | pdf]]
|-
| align="right" | 22.06.2017
| Software-defined Networking II
| Dr. M. Arumaithurai
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/ACN/2017_SS/p4_mayutan.pdf][https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/ACN/2017_SS/SDR.pdf][https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/ACN/2017_SS/nfv.pdf]
|-
| align="right" | 29.06.2017
| Information Centric Networks II
| Dr. M. Arumaithurai
| [https://projects.gwdg.de/projects/mayutan-public/repository/raw/courses/ACN/2017_SS/SAID-ACN.pdf]
|-
| align="right" | 06.07.2017
| Datacenter Networks
| Dr. D. Koll
| [[Media:ACN_SS2017_DCN.pdf | pdf]]
|-
| align="right" | 13.07.2017
| Written Examination (same time as the lecture. Room MN09, GZG)
|
|
|-
|}

==Prerequisites==
* Computer Science I, II; Computer Networks

[[Category:Courses]]