Machine Learning

Course Number 17165-01
Lecturers Volker Roth
Tutors Aleksander Wieczorek
Time and Location Mon 14:15 - 16:00; Seminarraum 00.003, Spiegelgasse 1
Thu 10:15 - 12:00; Seminarraum 00.003, Spiegelgasse 1
Start 16-02-2015
Exercises Mon 16:15 - 18:00; Computer-Labor U1.001, Spiegelgasse 1
Prerequisites Pattern Recognition; numerical analysis; statistics
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.
Literature tba
Assessment Lehrveranst.-begleitend
Credit Points 6
Grades 1-6 0,5
Modules Modul Kerninformatik (MSF - Informatik)
Modul Kerninformatik (Master Informatik 10)
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