IE 674: Hot Topics in Machine Learning (FSS 2020)


Lecture starts in the first week, tutorial in the second week. Lecture notes, exercises, assignments, and supplementary material can be found in ILIAS.


The tutorials start in the second week. There will be no tutorial on Sep 4.


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. This course builds upon IE 675 Machine Learning and introduces advanced algorithms, concepts, and theoretical principles. The course also focuses on selected “hot topics” and their applications. Tentative topics include:

  • Deep learning (models, applications, training methods, libraries)
  • Probababilistic models
  • Matrix and tensor decompositions
  • Graph analysis
  • AutoML
  • Active learning


  • 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