IE 675b: Machine Learning (HWS 2021)


The lecture will be held digitally, the tutorial once digitally and once in presence (when possible). Details are discussed in the kickoff lecture (Thursday, Sep 9, 12 noon).

Tutorials begin on the 2nd week, on Tuesday September 14th/Wednesday September 15th. You only need to attend one of the tutorials per week.


Machine learning is concerned with building computer systems that improve with experience as well as the study of learning processes, including the design of algorithms that are able to make predictions or extract knowledge from data. The aim of this module is to provide an introduction into the field of machine learning, and study algorithms, underlying concepts, and theoretical principles.

  • Basics of machine learning
  • Inference and prediction
  • Selected classification and regression models
  • Latent linear models
  • Mixture models and EM
  • Kernel methods
  • Probabilistic graphical models

More advanced topics are covered in the follow up course “Deep Learning”.

Lecture Notes

Lecture recordings, lecture notes, exercises, assignments, and supplementary material can be found in ILIAS.


  • K.P. Murphy. Machine Learning: A Probabilistic Perspective, The MIT Press, 2012 (4th printing)
  • D. Koller, N. Friedman. Probabilistic graphical models. The MIT Press, 2009
  • I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. The MIT Press, 2017 (
  • Additional material and articles provided in lecture notes