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

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* Monday, '''June 15th, 2015, 08.15 - 09.45''': Lecture 8
* Monday, '''June 15th, 2015, 08.15 - 09.45''': Lecture 8
** [[Media:ML_08.pdf | Instance-based learning (pdf)]]
** [[Media:ML_08.pdf | Instance-based learning (pdf)]]
* Monday, '''June 22nd, 2015, 08.15 - 09.45''': Lecture 7
* Monday, '''June 22nd, 2015, 08.15 - 09.45''': Lecture 9
* Monday, '''June 29th, 2015, 08.15 - 09.45''': Lecture 10
* Monday, '''June 29th, 2015, 08.15 - 09.45''': Lecture 10
* Monday, '''July 06th, 2015, 08.15 - 09.45''': Lecture 11
* Monday, '''July 06th, 2015, 08.15 - 09.45''': Lecture 11

Revision as of 10:09, 17 June 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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