Lecture: Machine Learning

Course Number 17165-01
Lecturers Volker Roth
Assistants Aleksander Wieczorek
Time and Location Tue 10:15 - 12:00; Seminarraum 00.003, Spiegelgasse 1
Wed 14:15 - 16:00; Hörsaal 114, Kollegienhaus
Start 21-02-2017
Exercises Wed 16:15 - 18:00; Hörsaal 114, Kollegienhaus
Prerequisites Knowledge and skills regarding pattern recognition, numerical analysis, and statistics
Objectives ? Understand the theoretical foundations of Machine Learning
? Understand and apply practical learning algorithms: linear and generalized linear models for regression and classification, Support Vector machines & kernel methods, mixture models & clustering.
? Program in Matlab
Contents Introduction: What is Machine Learning? Math refresher. Supervised Learning: theoretical foundations. Regression estimation: standard methods + algorithms. Classification: standard methods + algorithms. Learning Theory: risk minimization, regularization, elements of statistical learning theory. Kernel Methods. Mixture models. Conditional mixtures (mixtures of experts). Clustering. Bayesian model comparison.
Literature tba
Assessment Lehrveranst.-begleitend

Please note: Dates for the oral examinations: 21/22/23 June 2017, Spiegelgasse 1, office 06.003
Credit Points 8
Grades 1-6 0,5
Modules Modul Kerninformatik (MSF - Informatik (Studienbeginn vor 01.08.2016))
Modul Kerninformatik (Master Informatik 10)
Wahlbereich Master Informatik: Empfehlungen (Master Informatik 10)
Modul Praxis aktueller Informatikmethoden (MSF - Informatik (Studienbeginn vor 01.08.2016))
Modul Applications of Distributed Systems (Master Computer Science 16)
Modul Concepts of Machine Intelligence (Master Computer Science 16)
Modul Concepts of Machine Intelligence (MSF - Computer Science)
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