Module label  Machine Learning 
Modulkürzel  phy730 
Credit points  6.0 KP 
Workload  180 h
( Präsenzzeit: 56 Stunden Selbststudium: 124 Stunden )

Institute directory  Institute of Physics 
Verwendbarkeit des Moduls 

Zuständige Personen 
Lücke, Jörg (Prüfungsberechtigt)
Anemüller, Jörn (Prüfungsberechtigt)
Hohmann, Volker (Prüfungsberechtigt)
Meyer, Bernd (Prüfungsberechtigt)

Prerequisites  Basic knowledge in higher Mathematics as taught as part of first degrees in Physics, Mathematics, Statistics, Engineering or Computer Science (basic linear algebra and analysis). Basic programming skills (course supports matlab & python). Many relations to statistical physics, statistics, probability theory, stochastic but the course's content will be developed independently of detailed prior knowledge in these fields. 
Skills to be acquired in this module  The students will acquire advanced knowledge about mathematical models of data and ensory signals, and they will learn how such models can be used to derive algorithms for data and signal processing. They will learn the typical scientific challenges associated with algorithms for unsupervised knowledge extraction including, clustering, dimensionality reduction, compression and signal enhancements. Typical examples will include applications to computer vision and computer hearing. Furthermore, the students will learn modern interpretations of neural learning and neural perception based on probabilistic data models. 
Module contents  Introduction to unsupervised learning methods, i.e., methods that extract knowledge from data without the requirement of explicit knowledge about individual data points. We will introduce a common probabilistic framework for learning and a methodology to derive learning algorithms for different types of tasks. Examples that are derived are algorithms for clustering, classification, component extraction, feature learning, blind source separation and dimensionality reduction. Relations to neural network models and learning in biological systems will be discussed were appropriate. 
Literaturempfehlungen  C. M. Bishop, Pattern Recognition and Machine Learning, Springer 2006 (best suited for lecture). K. P. Murphy, Machine Learning: A Probabilistic Perspective, MIT Press, 2012. D. MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge University Press, 2003 (free online) K. Petersen, M. Pederson, The Matrix Cookbook, (free online) 
Links  
Language of instruction  English 
Duration (semesters)  1 Semester 
Module frequency  
Module capacity  unlimited 
Modullevel / module level  MM (Mastermodul / Master module) 
Modulart / typ of module  Pflicht / Mandatory 
Lehr/Lernform / Teaching/Learning method  Vorlesung: 2 SWS, Übungen: 2 SWS 
Vorkenntnisse / Previous knowledge 
Form of instruction  Comment  SWS  Frequency  Workload of compulsory attendance 

Lecture  2  WiSe  28  
Exercises  2  WiSe  28  
Präsenzzeit Modul insgesamt  56 h 
Examination  Prüfungszeiten  Type of examination 

Final exam of module  Klausur (max 180 Min.) oder mündliche Prüfung (30 Min.) 