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

Machine Learning for Pervasive Computing

Module #INF-KM-32
Event #INF-KM-43
ProgrammesMaster Informatik, Master Informations-Systemtechnik, Master Wirtschaftsinformatik
IBR Group(s)CM (Prof. Wolf)
TypeVorlesung/Übung
Lecturer
PhotoDr. Stephan Sigg
Researcher DAAD
sigg[[at]]ibr.cs.tu-bs.de
+49 531 3913249
Credits5
Hours2+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).
StartMittwoch 15. April, 11:30-13:00
Attendees Studenten der Informatik und Elektrotechnik, Wirtschaftsinformatik, Informations-Systemtechnik, Medienwissenschaften, Studenten mit Nebenfach Informatik
Prerequisiteskeine
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 ]
ChapterSlidesMoviesAudioExercises
0. Organisation
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1. Introduction
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2. Rule based
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3. Decision trees
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4. Regression
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exercise
5. Random search
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6. High dimensional data
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exercise
7. Artificial Neural Networks
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exercise
8. Instance-based learning
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exercise
9. Probabilistic graphical models
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10. Topic models
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11. Unsupervised learning
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12. Anomaly detection, recommendersystems, online learning
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Exkurs: Trendmining (Dr. Olga Streibel)
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Exkurs: Clustering and density-based clustering (Nguyen Thach)
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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 by Dr. Stephan Sigg
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