IE 675: Machine Learning (HWS 2020)


  • Lecturer:  Prof. Dr. Rainer Gemulla
  • Tutor: Daniel Ruffinelli
  • Type of course: Lecture, exercsises, assignments (6 ECTS points)
  • Prerequisites: IE 500 Data Mining I (recommended), knowledge of probability and statistics
  • Registration: enroll in ILIAS

The course will be held digitally. Details are discussed in the kickoff lecture on Oct 1, 12:00–13:30, in WIM-ZOOM-09.


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
  • Kernels
  • Probabilistic graphical models

More advanced topics are covered in the follow up course “IE 674 Hot Topics in Machine 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