Machine Learning and Pervasive Computing (Winter 2014/2015): Difference between revisions

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== Details ==
== Details ==
{{CourseDetails
{{CourseDetails
|credits=180h, 6 ECTS
|credits=180h, 5 ECTS
|module=M.Inf.1223: Spezielle fortgeschrittene Aspekte der Computernetzwerke
|module=M.Inf.1223: Spezielle fortgeschrittene Aspekte der Computernetzwerke
|lecturer=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&moduleCall=webInfo&publishConfFile=webInfoPerson&publishSubDir=personal&keep=y&purge=y&personal.pid=128205 Stephan Sigg]
|lecturer=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&moduleCall=webInfo&publishConfFile=webInfoPerson&publishSubDir=personal&keep=y&purge=y&personal.pid=128205 Stephan Sigg]
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==Schedule==
==Schedule==
* Wednesday, '''October 22nd, 2014, 10.15 - 11.45''': Lecture 1
* Wednesday, '''October 22nd, 2014, 10.15 - 11.45''': Lecture 1
** [[Media:MLPC_00.pdf | Organisation (pdf)]]
** [[Media:MLPC_01.pdf | Introduction (pdf)]]
* Wednesday, '''October 29th, 2014, 10.15 - 11.45''': Lecture 2
* Wednesday, '''October 29th, 2014, 10.15 - 11.45''': Lecture 2
** [[Media:MLPC_02.pdf | Classification and Evaluation (pdf)]]
* Wednesday, '''November 5th, 2014, 10.15 - 11.45''': Lecture 3
* Wednesday, '''November 5th, 2014, 10.15 - 11.45''': Lecture 3
** [[Media:MLPC_03.pdf | Simple supervised learning (pdf)]]
* Wednesday, '''November 5th, 2014, 16.15 - 17.45''': Exercise 1
* Wednesday, '''November 5th, 2014, 16.15 - 17.45''': Exercise 1
** [[Media:MLPC_Assignment_01.pdf | Assignment 1 (pdf)]]
* Wednesday, '''November 12th, 2014, 10.15 - 11.45''': Lecture 4
* Wednesday, '''November 12th, 2014, 10.15 - 11.45''': Lecture 4
** [[Media:MLPC_04.pdf | Random search (pdf)]]
* Wednesday, '''November 19th, 2014''': No lecture
* Wednesday, '''November 19th, 2014''': No lecture
* Wednesday, '''November 26th, 2014, 10.15 - 11.45''': Lecture 5
* Wednesday, '''November 26th, 2014, 10.15 - 11.45''': Lecture 5
** [[Media:MLPC_05.pdf | Bayesian networks and Naive Bayes (pdf)]]
* Wednesday, '''November 26th, 2014, 16.15 - 17.45''': Exercise 2
** [[Media:MLPC_Assignment_02.pdf | Assignment 2 (pdf)]]
* Wednesday, '''December 3rd, 2014''': No lecture
* Wednesday, '''December 3rd, 2014''': No lecture
* Wednesday, '''December 10th, 2014, 10.15 - 11.45''': Lecture 6   
* Wednesday, '''December 10th, 2014, 10.15 - 11.45''': Lecture 6   
** [[Media:MLPC_06.pdf | Decision Tree (pdf)]]
* Wednesday, '''December 17th, 2014, 10.15 - 11.45''': Lecture 7
* Wednesday, '''December 17th, 2014, 10.15 - 11.45''': Lecture 7
* Wednesday, '''December 17th, 2014, 16.15 - 17.45''': Exercise 2
** [[Media:MLPC_07.pdf | K-nearest neighbour (pdf)]]
* Wednesday, '''December 17th, 2014, 16.15 - 17.45''': Exercise 3
** [[Media:MLPC_Assignment_03.pdf | Assignment 3 (pdf)]]
* Wednesday, '''January 7th, 2015, 10.15 - 11.45''': Lecture 8
* Wednesday, '''January 7th, 2015, 10.15 - 11.45''': Lecture 8
** [[Media:MLPC_08.pdf | Support Vector Machines (pdf)]]
* Wednesday, '''January 14th, 2015, 10.15 - 11.45''': Lecture 9
* Wednesday, '''January 14th, 2015, 10.15 - 11.45''': Lecture 9
* Wednesday, '''January 14th, 2015, 16.15 - 17.45''': Exercise 3
** [[Media:MLPC_09.pdf | Artificial neural networks and self organizing maps (pdf)]]
* Wednesday, '''January 14th, 2015, 16.15 - 17.45''': Exercise 4
* Wednesday, '''January 21st, 2015, 10.15 - 11.45''': Lecture 10
* Wednesday, '''January 21st, 2015, 10.15 - 11.45''': Lecture 10
** [[Media:MLPC_10.pdf | Hidden markov models and conditional random fields (pdf)]]
* Wednesday, '''January 28th, 2015, 10.15 - 11.45''': Lecture 11
* Wednesday, '''January 28th, 2015, 10.15 - 11.45''': Lecture 11
* Wednesday, '''January 28th, 2015, 16.15 - 17.45''': Exercise 4
** [[Media:MLPC_11.pdf | Dimensionality reduction and unsupervised learning (pdf)]]
* Wednesday, '''January 28th, 2015, 16.15 - 17.45''': Exercise 5
* Wednesday, '''February 4th, 2015, 10.15 - 11.45''': Lecture 12
* Wednesday, '''February 4th, 2015, 10.15 - 11.45''': Lecture 12
** [[Media:MLPC_12.pdf | Anomaly detection, online learning and recommender systems (pdf)]]


==Requirements==
==Requirements==

Latest revision as of 18:19, 9 February 2015

Details

Workload/ECTS Credits: 180h, 5 ECTS
Module: M.Inf.1223: Spezielle fortgeschrittene Aspekte der Computernetzwerke
Lecturer: Stephan Sigg
Teaching assistant: --
Time: Wednesdays, 10.15 - 11.45.; Exercise: 16:15 - 17:45 (bi-weekly)
Place: IfI 3.101
UniVZ [1]


Course Overview

The course will address selected topics in Pervasive Computing. This semester the main focus of the lecture will be on Machine learning and activity recognition from sensor-data. In addition, other fields of Pervasive Computing are covered to provide students with a good overview on current advances and research challenges. Depending on the interest of the students, the emphasis on these additional topics may differ.

  • Course topics
    • Introduction to Machine learning
    • Supervised and Unsupervised learning
    • Features and feature extraction
    • Feature subset selection
    • Performance metrics
    • Polynomial curve fitting
    • Support Vector Machines
    • Artificial Neural Network learning
    • Clustering (k-means)
    • Dimensionality reduction
    • Anomaly detection
    • Recommender systems



Schedule

Requirements

  • Each participant is required to attend and actively participate in the exercises.
  • Oral examination at the end of the semester

Reading List

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