Theoretical Concepts of Machine Learning (2VL)

Course no.: 365.041
Lecturer: Sepp Hochreiter
Times/locations: Thur 15:30-17:00, room S3 055
Start: Thur, March 8, 2018
Mode: VL, 2h, weekly
Registration: KUSSS
Exams: 3 written part-exams, register via KUSSS.
Retry Exam on July 12th, register via KUSSS

Lecture notes:

PDF (5MB, 2014-03-02)


Part1 (7MB)
Part2 (4MB)


Machine learning is concerned inferring models/relationships by learning from data. Machine learning methods are gaining importance in various fields, such as, process modeling, speech and image processing, and so forth. In recent years, bioinformatics has become one of the most prominent application areas of machine learning methods: The massive data amounts produced by recent and currently emerging high-throughput biotechnologies provide unprecedented potentials, but also pose yet unseen computational challenges in the analysis of biological data. Despite all potentials and successes of machine learning, one has to acknowledge that machine learning methods may produce poor or misleading results if they are applied inappropriately.

This course provides a look at the theoretical background of machine learning. The goal is to make students acquainted with the mathematical theories underlying machine learning methods in order to have a more profound understanding of the potentials and limits of machine learning. Topics: (Practical course Theoretical Concepts of Machine Learning (1UE))