Credit: Anna Logue
IE 675: Machine Learning (HWS 2019)
News
- Registration in ILIAS has been opened.
- Tutorials start in the second week, i.e., there is no tutorial an Sep 3.
Organization
- Lecturer: Prof. Dr. Rainer Gemulla, Kiril Gashteovski
- Type of course: Lecture, exercsises, assignments (6 ECTS points)
- Prerequisites: IE 500 Data Mining I (recommended), knowledge of probability and statistics
- Registration: enroll in ILAS
Content
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
- Introduction to neural networks
Lecture Notes
Lecture notes, exercises, assignments, and supplementary material can be found in ILIAS.
Literature
- 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 (http://www.deeplearningbook.org/)
- Additional material and articles provided in lecture notes