IE 674 Hot Topics in Machine Learning (HWS 2017)


  • Lecturer: Prof. Dr. Rainer Gemulla, Yanjie Wang
  • Type of course: Lecture, exercsises, assignments (6 ECTS points)
  • Lecture: Wednesday, 08:30-10:00, B6, A101
  • Tutorial: Tuesday, 10:15-11:45, B6, A101 (starts Sep 19)
  • Evaluation: Oral examination (prerequisite: assignments)
  • Prerequisites: IE 500 Data Mining I (recommended), IE 560 Decision support (recommended), basic knowledge of probability and statistics
  • Registration: enroll in ILAS



Machine Learning is about designing algorithms that are able to make predictions about data or extract knowledge from data. The aim of this module is to study algorithms, underlying concepts, and theoretic principles in machine learning. The course focuses on selected "hot topics" and their applications, which include:

  • Basics of machine learning and probability theory
  • Probabilistic graphical models
  • Inference and parameter estimation
  • Neural networks

Lecture Notes

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

  • 00: Organization (pdf)
  • 01: Introduction (pdf)
  • 02: Probability (pdf)
  • 03: Logistic regression (pdf)
  • 04: Naive Bayes (pdf)
  • 05: Undirected Graphical Models (pdf)
  • 06: Conditional Random Fields (pdf)
  • 07: Neural Networks (pdf)
  • 08: Wrap Up (pdf)


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