Version vom 17. Mai 2023, 15:23 Uhr

Vorlesung Maschinelles Lernen 1 - Grundverfahren

Details zur Lehrveranstaltung

Dozent(en)	J. Marius Zöllner
Übungsleiter	Nikolai Polley, Marcus Fechner, Mohammd Karam Daaboul
Fach (Gebiet)	Informatik, Maschinelles Lernen, Künstliche Intelligenz
Leistungspunkte	ECTS
Erfolgskontrolle	Klausur
Semester	WS

Aktuelle und ergänzende Informationen, sowie Zeiten und Räume der Lehrveranstaltung finden Sie im Vorlesungsverzeichnis der Universität.
Link zum Vorlesungsverzeichnis
Link zum Studierendenportal

Forschungsgruppe

Angewandte Technisch-Kognitive Systeme

Literatur

Machine Learning - Tom Mitchell
Deep Learning - Ian Goodfellow, Yoshua Bengio, Aaron Courville
Pattern Recognition and Machine Learning - Christopher M. Bishop
Artificial Intelligence: A Modern Approach - Peter Norvig and Stuart J. Russell
Reinforcement Learning: An Introduction - Richard S. Sutton and Andrew G. Barto

Weitere (spezifische) Literatur zu einzelnen Themen wird in der Vorlesung angegeben.

@@ Zeile 24: / Zeile 24: @@
 * Studierende können Methoden des Maschinellen Lernens einordnen, formal beschreiben und bewerten.
 * Die Studierenden können ihr Wissen für die Auswahl geeigneter Modelle und Methoden für ausgewählte Probleme im Bereich des Maschinellen Lernens einsetzen.
-|Inhalt EN=The course prepares students for the rapidly evolving field of machine learning by providing a solid foundation, covering core concepts and techniques to get started in the field. Students delve into different methods in supervised, unsupervised, and reinforcement learning, as well as various model types, ranging from basic linear classifiers to more complex methods, such as deep neural networks. Topics include general learning theory, support vector machines, decision trees, neural network fundamentals, convolutional neural networks, recurrent neural networks, unsupervised learning, reinforcement learning, and Bayesian learning.
-The course is accompanied by a corresponding exercise, where students gain hands-on experience by implementing and experimenting with different machine learning algorithms, helping them to apply machine learning algorithms on real world problems.
-By the end of the course, students will have acquired a solid foundation in machine learning, enabling them to apply state-of-the-art algorithms to solve complex problems, contribute to research efforts, and explore advanced topics in the field.
-<b>Learning obectives</b>:
-* Students acquire knowledge of the fundamental methods in the field of machine learning.
-* Students can classify, formally describe and evaluate methods of machine learning.
-* Students can use their knowledge to select suitable models and methods for selected problems in the field of of machine learning.
 |Literatur=* Machine Learning - Tom Mitchell
 * Deep Learning - Ian Goodfellow, Yoshua Bengio, Aaron Courville

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Version vom 17. Mai 2023, 15:23 Uhr

Vorlesung Maschinelles Lernen 1 - Grundverfahren