Machine Learning for Pervasive Computing

Lecture slides

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Kapitel	Folien	Filme	Audio	Übungen
0. Organisation
1. Introduction
2. Rule based
3. Decision trees
4. Regression
5. Random search
6. High dimensional data
7. Artificial Neural Networks
8. Instance-based learning
9. Probabilistic graphical models
10. Topic models
11. Unsupervised learning
12. Anomaly detection, recommendersystems, online learning
Exkurs: Trendmining (Dr. Olga Streibel)
Exkurs: Clustering and density-based clustering (Nguyen Thach)

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.