Machine Learning and Pervasive Computing (Winter 2014/2015)

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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



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

Reading List