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Machine Learning (WS 2023/24)


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Time and Locations

All times are c.t. (cum tempore)

Component When Where

Starts at



Thu, 9:00 c.t. - 12:00 h

Mon, 16:00 c.t. - 18:00

Geschw.-Scholl-Pl. 1 - B 006

Geschw.-Scholl-Pl. 1 - M 110




Machine Learning is a data-driven approach for the development of technical solutions. Initially motivated by the adaptive capabilities of biological systems, machine learning has increasing impact in many fields, such as vision, speech recognition, machine translation, and bioinformatics, and is a technological basis for the emerging field of Big Data.

The lecture will cover:

      • Supervised learning: the goal here is to learn functional dependencies for classification and regression. We cover linear systems, basis function approaches, kernel approaches and neural networks. We will cover the recent developments in deep learning which lead to exciting applications in speech recognition and vision.
      • Unsupervised Learning: the goal here is to compactly describe important structures in the data. Typical representatives are clustering and principal component analysis
      • Graphical models (Bayesian networks, Markov networks), which permit a unified description of high-dimensional probabilistic dependencies
      • Reinforcement Learning as the basis for the learning-based optimization of autonomous agents
      • Some theoretical aspects: frequentist statistics, Bayesian statistics, statistical learning theory

The technical topics will be illustrated with a number of real-world applications.

Course Materials

Final Examination

Recommended Literature:

      • Deep Learning. Ian Goodfellow, Yoshua Bengio and Aaron Courville
      • The Elements of Statistical Learning: Data mining, Inference and Prediction. Hastie, Tibshirani, Friedman
      • Machine Learning: a Probabilistic Perspective. Kevin Murphy
      • Bayesian Reasoning and Machine Learning. David Barber
      • Pattern Recognition and Machine Learning. Christopher M. Bishop
      • Artificial Intelligence: A Modern Approach. Russel and Norvig

Links to Tutorials