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

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* 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)]]
* 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)]]
* 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
* Monday, '''June 22nd, 2015, 08.15 - 09.45''': Lecture 7

Revision as of 10:27, 8 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