Machine Learning and Pervasive Computing (Summer 2015)

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Details

Workload/ECTS Credits: 180h, 5 ECTS
Module: M.Inf.1223: Spezielle fortgeschrittene Aspekte der Computernetzwerke
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

  • Monday, April 13th, 2015, 08.15 - 09.45: Lecture 1
  • Monday, April 20th, 2015, 08.15 - 09.45: Lecture 2
  • Monday, April 27th, 2015, 08.15 - 09.45: Lecture 3
  • Monday, May 04th, 2015, 08.15 - 09.45: Lecture 4
  • Monday, May 11th, 2015, 08.15 - 09.45: No lecture
  • Monday, May 18th, 2015, 08.15 - 09.45: Lecture 5
  • Monday, May 25th, 2015, 08.15 - 09.45: No lecture
  • Monday, June 01st, 2015, 08.15 - 09.45: Lecture 6
  • Monday, June 08th, 2015, 08.15 - 09.45: Lecture 7
  • Monday, June 15th, 2015, 08.15 - 09.45: Lecture 8
  • Monday, June 22nd, 2015, 08.15 - 09.45: Lecture 7
  • Monday, June 29th, 2015, 08.15 - 09.45: Lecture 10
  • Monday, July 06th, 2015, 08.15 - 09.45: Lecture 11
  • Monday, July 13th, 2015, 08.15 - 09.45: Lecture 12

Requirements

  • Active participation in the exercises required.

Reading List