Machine Learning: Unsupervised Techniques (2VL)

Course no.: 365.077
Lecturer: Sepp Hochreiter
Times/locations: Mon 15:30-17:00, room S3 055
Start: Mon, March 6, 2017
Mode: VL, 2h, weekly
Registration: KUSSS
Exam: Mon 26.6.2017, 15:30-17:00, S3 055, register via KUSSS.

Lecture notes:

PDF (20MB, 2014-03-02)


Part1 (2MB, 2014-03-02)
Part2 (15MB, 2014-03-02)
Part3 (16MB, 2014-03-02)


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.

This course focuses on so-called unsupervised machine learning techniques, that is, methods aiming at inferring structure/models in data without an explicit target. The students should aquire skills to choose, use, and adapt methods for clustering, data projection, and data reduction for tasks in science and engineering. The students should particularly understand the underlying mathematical objectives and principles of unsupervised machine learning methods. Topics: (Practical course Machine Learning: Unsupervised Techniques (1UE))