TU BRAUNSCHWEIG
| Carl-Friedrich-Gauß-Faculty | Computer Science
Informatikzentrum

Machine Learning for Pervasive Computing

Semester Summer 2015 [ Other terms: ]
Module # INF-KM-32
Event # INF-KM-43
Programmes Master Informatik, Master Informations-Systemtechnik, Master Wirtschaftsinformatik
IBR Group(s) CM (Prof. Wolf)
Type Vorlesung/Übung
Lecturer
Photo Dr. Stephan Sigg
Researcher DAAD
sigg[[at]]ibr.cs.tu-bs.de
+49 531 3913249
Credits 5
Hours 2+1
Time & Place Lecture: Wednesday, 11:30-13:00 Room IZ-358
Exercise: Wednesday, 13:15-14:45, Room IZ-358
This lecture is discontinued. If you are interested in Machine Learning and Applications please consider the Lecture Machine Learning for Computer Security (Institute of System Security).
Start Mittwoch 15. April, 11:30-13:00
Attendees Studenten der Informatik und Elektrotechnik, Wirtschaftsinformatik, Informations-Systemtechnik, Medienwissenschaften, Studenten mit Nebenfach Informatik
Prerequisites keine
Certificates Successful oral examination. Furthermore, active participation in the exercises is expected.
Content

Lecture slides

The material is only available to registered attendees. In order to register, you need either an IBR account or a self-activated IBR-y-account. Afterwards you can login to this site (with the function at the top of this page).
[ Podcast | Podcast aller Formate | iPod-Video-Podcast | Audio-Podcast | Newsfeed aller Formate ]
Chapter Slides Movies Audio Exercises
0. Organisation
pdf pdf pdf
1. Introduction
pdf pdf pdf
2. Rule based
pdf pdf pdf
3. Decision trees
pdf pdf pdf
4. Regression
pdf pdf pdf
exercise
5. Random search
pdf pdf pdf
6. High dimensional data
pdf pdf pdf
exercise
7. Artificial Neural Networks
pdf pdf pdf
exercise
8. Instance-based learning
pdf pdf pdf
exercise
9. Probabilistic graphical models
pdf pdf pdf
10. Topic models
pdf pdf pdf
11. Unsupervised learning
pdf pdf pdf
12. Anomaly detection, recommendersystems, online learning
pdf pdf pdf
Exkurs: Trendmining (Dr. Olga Streibel)
pdf pdf pdf
Exkurs: Clustering and density-based clustering (Nguyen Thach)
pdf pdf pdf

Lecture

We investigate popular algorithms applied for supervised and unsupervised machine learning on real data and consider test-set design principles, model and feature selection as well as performance metrics. The discussed considered cover linear/logistic/multivariate/multivariable regression, Support vector machines, artificial neural networks, k-nearest neighbour, k-means, self organizing maps, decision trees, naive Bayes and Bayesian networks, hidden markov models, conditional random fields and principle component analysis.

The course will address selected topics in Pervasive Computing with a special focus 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

Literature

  • Bishop, Christopher M. Pattern recognition and machine learning. Vol. 1. New York: springer, 2006.
  • Duda, Richard O., Peter E. Hart, and David G. Stork. Pattern classification. John Wiley and Sons, 2012.
  • Witten, Ian H., and Eibe Frank. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, 2005.

last changed 2016-04-05, 08:28
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