phy730 Machine Learning (Vollständige Modulbeschreibung)

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.

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.

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.

- 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)

Aufnahmekapazität:
unbegrenzt

Lehr-/Lernform:
Vorlesung: 2 SWS, Übungen: 2 SWS