Semester | Summer 2015 |
Module # | INF-KM-32 |
Event # | INF-KM-43 |
Programmes | Computer Science Master, Computer and Communication Systems Engineering Master, Business Information Systems Master |
IBR Group | CM (Prof. Wolf) |
Type | Lecture & Exercise |
Lecturer | |
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 slidesThe material is only available to registered attendees. In order to register, you need either an IBR POSIX account or a self-activated IBR-y-account. Afterwards you can login to this site. Chapter Slides Movies Exercises 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) LectureWe 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
Literature
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