IE 674 Hot Topics in Machine Learning (HWS 2018)

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

Machine learning studies how to enable machines to learn from data and experience, e.g., in order to make predictions 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 models
• Inference and parameter estimation
• Neural networks

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)
• I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. The MIT Press, 2017 (http://www.deeplearningbook.org/)
• D. Koller, N. Friedman. Probabilistic graphical models. The MIT Press, 2009
• Additional material and articles provided in lecture notes