Machine Learning and Pervasive Computing (Summer 2015): Difference between revisions

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{{CourseDetails
{{CourseDetails
|credits=180h, 5 ECTS
|credits=180h, 5 ECTS
|module=M.Inf.1223: Spezielle fortgeschrittene Aspekte der Computernetzwerke
|module=M.Inf.1223: Spezielle fortgeschrittene Aspekte der Computernetzwerke; ITIS: 3.33
|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]
|ta= --
|ta= --
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** [[Media:MLnotes_01.pdf | Lecture notes (Introduction) (pdf)]]
** [[Media:MLnotes_01.pdf | Lecture notes (Introduction) (pdf)]]
* Monday, '''April 20th, 2015, 08.15 - 09.45''': Lecture 2
* Monday, '''April 20th, 2015, 08.15 - 09.45''': Lecture 2
** [[Media:ML_02.pdf | Rule-based (pdf)]]
* Monday, '''April 27th, 2015, 08.15 - 09.45''': Lecture 3
* Monday, '''April 27th, 2015, 08.15 - 09.45''': Lecture 3
** [[Media:ML_03.pdf | Decision tree (pdf)]]
** [[Media:ML_practical_01.pdf | Projects meeting (Group allocation) (pdf)]]
* Monday, '''May 04th, 2015, 08.15 - 09.45''': Lecture 4
* Monday, '''May 04th, 2015, 08.15 - 09.45''': Lecture 4
** [[Media:ML_04.pdf | Regression (pdf)]]
** [[Media:MLnotes_04.pdf | Lecture notes (Regression) (pdf)]]
** [[Media:MLassignment_01.pdf | Assignment 01 (pdf)]]
* Monday, '''May 11th, 2015, 08.15 - 09.45''': No lecture
* Monday, '''May 11th, 2015, 08.15 - 09.45''': No lecture
* Monday, '''May 18th, 2015, 08.15 - 09.45''': Lecture 5
* Monday, '''May 18th, 2015, 08.15 - 09.45''': Lecture 5
** [[Media:ML_05.pdf | Local random search (pdf)]]
** [[Media:MLassignment_02.pdf | Assignment 02 (pdf)]]
* Monday, '''May 25th, 2015, 08.15 - 09.45''': No lecture
* Monday, '''May 25th, 2015, 08.15 - 09.45''': No lecture
* Monday, '''June 01st, 2015, 08.15 - 09.45''': Lecture 6
* Monday, '''June 01st, 2015, 08.15 - 09.45''': Lecture 6
** [[Media:ML_06.pdf | High dimensional data (pdf)]]
** [[Media:MLnotes_06.pdf | Lecture notes (High dimensional data) (pdf)]]
** [[Media:MLassignment_03.pdf | Assignment 03 (pdf)]]
* Monday, '''June 08th, 2015, 08.15 - 09.45''': Lecture 7
* Monday, '''June 08th, 2015, 08.15 - 09.45''': Lecture 7
** [[Media:ML_07.pdf | Artificial Neural Networks (pdf)]]
** [[Media:MLnotes_07.pdf | Lecture notes (Artificial Neural Networks) (pdf)]]
* Monday, '''June 15th, 2015, 08.15 - 09.45''': Lecture 8
* Monday, '''June 15th, 2015, 08.15 - 09.45''': Lecture 8
* Monday, '''June 22nd, 2015, 08.15 - 09.45''': Lecture 7
** [[Media:ML_08.pdf | Instance-based learning (pdf)]]
** [[Media:MLassignment_04.pdf | Assignment 04 (pdf)]]
* Monday, '''June 22nd, 2015, 08.15 - 09.45''': Lecture 9
** [[Media:ML_09.pdf | Probabilistic Graphical Models (pdf)]]
* Monday, '''June 29th, 2015, 08.15 - 09.45''': Lecture 10
* Monday, '''June 29th, 2015, 08.15 - 09.45''': Lecture 10
** [[Media:ML_10.pdf | Topic Models (pdf)]]
* Monday, '''July 06th, 2015, 08.15 - 09.45''': Lecture 11
* Monday, '''July 06th, 2015, 08.15 - 09.45''': Lecture 11
** [[Media:ML_11.pdf | Unsupervised Learning (pdf)]]
** [[Media:ML_11-2.pdf | Clustering and density based clustering (Thach Nguyen) (pdf)]]
* Monday, '''July 13th, 2015, 08.15 - 09.45''': Lecture 12
* Monday, '''July 13th, 2015, 08.15 - 09.45''': Lecture 12
** [[Media:ML_12.pdf | Anomaly detection, Online learning, Recommender Systems (pdf)]]


==Requirements==
==Requirements==

Latest revision as of 09:09, 6 July 2015

Details

Workload/ECTS Credits: 180h, 5 ECTS
Module: M.Inf.1223: Spezielle fortgeschrittene Aspekte der Computernetzwerke; ITIS: 3.33
Lecturer: Stephan Sigg
Teaching assistant: --
Time: Mondays, 08.15 - 09.45.; Exercise: (bi-weekly)
Place: IfI 3.101
UniVZ [1]


Course Overview

The course will give a comprehensive overview on Machine learning with applications in Pervasive Computing.

  • Course topics
    • Introduction to Machine learning
    • Features and feature extraction
    • Feature subset selection
    • Performance metrics
    • Rule-based learning
    • Regression approaches
    • High dimensional data
    • Artificial Neural Network learning
    • Probabilistic approaches
    • Topic models
    • nearest neighbour methods
    • Unsupervised learning and Clustering
    • Anomaly detection
    • Recommender systems

Schedule

Requirements

  • Active participation in the exercises required.

Reading List

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