Practical Course Advanced Networking (Summer 2016): Difference between revisions
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|module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke | |module=M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke | ||
|lecturer=[http://www.net.informatik.uni-goettingen.de/people/david_koll Dr. David Koll] | |lecturer=[http://www.net.informatik.uni-goettingen.de/people/david_koll Dr. David Koll] | ||
|ta= | |ta=Yali Yuan, MSc. | ||
|time=start:April | |time=start:April 18, 10-12 | ||
|place= | |place=IFI 3.101 | ||
|univz=tba | |univz=tba | ||
| | | | ||
}} | }} | ||
{{Announcement|Note that the introduction meeting and announcement of the first task has been shifted to April 18, 10-12am in room 3.101.}} | |||
==Course Organization== | ==Course Organization== | ||
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==Prerequisites== | ==Prerequisites== | ||
You are ''highly recommended'' to have completed a course on Data Science (e.g., "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 classification, etc.) | *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.) | |||
==Passing requirements== | ==Passing requirements== | ||
* Solve the warmup-problem (10% of final grade, this is also required in order to continue the course) | * Solve the warmup-problem (10% of final grade, this is also required in order to continue the course) | ||
* Present your task specific findings (2*35% = 70% in total) | * Present your task specific findings (2*35% = 70% in total) | ||
* Prepare a '''written report''' on the | * Prepare a '''written report''' on the work done in the course (15-20 pages containing the most important steps taken and their results, Template:[ftp://ftp.springer.de/pub/tex/latex/llncs/latex2e/llncs2e.zip]) (20%) | ||
* It is mandatory for all students to '''stick to the deadlines''' mentioned in [[#Schedule]]'''. | * It is mandatory for all students to '''stick to the deadlines''' mentioned in [[#Schedule]]''' and to attend other teams' presentations. | ||
==Slides and Task Descriptions== | |||
==Schedule== | ==Schedule== | ||
* ''' | * '''18 April 2016 (Monday), 10-12''': Informational meeting | ||
** Introduction to the course, formation of teams, and discussion of open questions | ** Introduction to the course, formation of teams, and discussion of open questions | ||
* ''' | * '''18 April - 7 May''': Let's make sure we're on the same page | ||
** You will get a warmup-task with a simple dataset that you should analyse descriptively, and then build a machine learning predictor on. This is to ensure that you meet the course prerequisites. Students who fail this step will not be allowed to continue the course. | ** You will get a warmup-task with a simple dataset that you should analyse descriptively, and then build a machine learning predictor on. This is to ensure that you meet the course prerequisites. Students who fail this step will not be allowed to continue the course. | ||
* ''' | * '''7 May''': Submission on warmup task due (submit to yali [dot] yuan [at] informatik.uni-goettingen.de) | ||
* '''2 May - 9 June''': Working on task # | * '''2 May - 9 June''': Working on task #2 | ||
* '''9 June, 14-16''': Presentation on task # | * '''9 June, 14-16''': Presentation on task #2 | ||
* '''13 June - 14 July''': Working on task # | * '''13 June - 14 July''': Working on task #3 | ||
* '''14 July, 14-16''' Presentation on task # | * '''14 July, 14-16''' Presentation on task #3 | ||
* '''30 September:''' Submission of final report. | * '''30 September:''' Submission of final report. | ||
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== Tasks == | == Tasks == | ||
To ensure that all teams have the same time available for each task, the details will be published at the start of each phase (e.g., warmup task will be provided on April | To ensure that all teams have the same time available for each task, the details will be published at the start of each phase (e.g., warmup task will be provided on April 18th) | ||
Latest revision as of 12:26, 18 October 2016
Details
| Workload/ECTS Credits: | 180h, 6 ECTS |
| Module: | M.Inf.1800 Fortgeschrittenen Praktikum Computernetzwerke |
| Lecturer: | Dr. David Koll |
| Teaching assistant: | Yali Yuan, MSc. |
| Time: | start:April 18, 10-12 |
| Place: | IFI 3.101 |
| UniVZ | tba |
|
Note that the introduction meeting and announcement of the first task has been shifted to April 18, 10-12am in room 3.101. |
Course Organization
In this course, you will form teams of 2-3 students (depending on the number of course attendees) with the goal of completing 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., "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.)
Passing requirements
- Solve the warmup-problem (10% of final grade, this is also required in order to continue the course)
- Present your task specific findings (2*35% = 70% in total)
- Prepare a written report on the work done in the course (15-20 pages containing the most important steps taken and their results, Template:[1]) (20%)
- It is mandatory for all students to stick to the deadlines mentioned in #Schedule and to attend other teams' presentations.
Slides and Task Descriptions
Schedule
- 18 April 2016 (Monday), 10-12: Informational meeting
- Introduction to the course, formation of teams, and discussion of open questions
- 18 April - 7 May: Let's make sure we're on the same page
- You will get a warmup-task with a simple dataset that you should analyse descriptively, and then build a machine learning predictor on. This is to ensure that you meet the course prerequisites. Students who fail this step will not be allowed to continue the course.
- 7 May: Submission on warmup task due (submit to yali [dot] yuan [at] informatik.uni-goettingen.de)
- 2 May - 9 June: Working on task #2
- 9 June, 14-16: Presentation on task #2
- 13 June - 14 July: Working on task #3
- 14 July, 14-16 Presentation on task #3
- 30 September: Submission of final report.
Time limits for presentations will range between 15 and 25 minutes per team. Exact durations will be set dynamically depending on the number of student teams in the course.
Tasks
To ensure that all teams have the same time available for each task, the details will be published at the start of each phase (e.g., warmup task will be provided on April 18th)