IE 675b: Machine Learning (HWS 2023)

Organization

Lecturer: Prof. Dr. Rainer Gemulla
Tutor: Tommaso Green, Daniel Ruffinelli
Type of course: Lecture, exercises, assignments (9 ECTS points)
Prerequisites: IE 500 Data Mining I (recommended / in parallel), knowledge of probability and statistics
Registration: enroll in Portal 2

The lecture will be held as inverted classroom with lecture videos and one weekly in-presence session. The tutorials will be held in presence and start in the second week. Details are discussed in the kickoff lecture (Thursday, Sep 7, 12:00).

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
Latent linear models
Mixture models and EM
Kernel methods
Probabilistic graphical models

Deep learning and more advanced topics are covered in the follow up course “IE 678 Deep Learning”.

Lecture Notes

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

Literature

K.P. Murphy. Probabilistic Machine Learning: An Introduction. The MIT Press, 2022 (https://probml.github.io/pml-book/book1.html)
I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. The MIT Press, 2017 (http://www.deeplearningbook.org/)
Additional material and articles provided in lecture notes