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.

Learning obectives:

• 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.

Die Foliensätze sind als PDF verfügbar

Weiterführende 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.