Diese Vorlesung ist Teil des Informatik Bachelor Studiengangs. Der Kurs wird von der Autonomous Learning Group in der Abteilung Distributed Intelligence angeboten.
In dieser Vorlesung werden grundlegende Prinzipien und einfache Algorithmen aus dem Bereich des statistischen Lernens vermittelt. Themen sind u.a.:
Verschiedene Lernprobleme und Ansätze zur Lösung, Grundprinzipien des statistischen Lernens (Satz von Bayes, Entscheidungstheorie, grundlegende Probleme, Evaluation von Ergebnissen), einfache Modelle aus dem Bereich des überwachten und unüberwachten Lernens (Dichteschätzung, Klassifizierung, Clustering), ML im gesellschaftlichen Kontext. Wir gehen auch auf aktuelle Architekturen und Verfahren ein.
Die Studierenden kennen grundlegende Prinzipien und Verfahren des maschi-
nellen Lernens und wissen um deren prinzipiellen Grenzen. In den Übungen
haben sie gelernt, kleine praktische Probleme mit den behandelten Verfahren
zu lösen und entsprechende Algorithmen praktisch zu implementieren.
Sowohl die Folien als auch die Übungen sind auf ILIAS verfügbar.
Wir verwenden teilweise das Buch 'Introduction to Machine Learning', 4th Edition, Ethem Alpaydin, MIT Press. Chapters 1-12 and 20. und 'Pattern Recognition and Machine Learning' by Christopher Bishop, www.microsoft.com/en-us/research/people/cmbishop/prml-book
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