Course details


Independent study course: Information Theory

Independent Study Course
SS 2020 Ulf Krumnack, Axel Schaffland
B.Sc modules:
CS-BWP-AI - Artificial Intelligence
CS-BWP-NI - Neuroinformatics
KOGW-WPM-KI - Artificial Intelligence
KOGW-WPM-NI - Neuroinformatics
M.Sc modules:
CC-MP-IDC - Interdisciplinary Course
CC-MWP-AI - Artificial Intelligence
CC-MWP-NI - Neuroinformatics
CS-MP-IDC - Interdisciplinary Course
CS-MWP-AI - Artificial Intelligence
CS-MWP-NI - Neuroinformatics
KOGW-MPM-IDK - Interdisciplinary courses
KOGW-MWPM-KI - Major subject Artificial Intelligence
KOGW-MWPM-NIR - Major subject Neuroinformatics and Robotics
Fri: 8-10

Since its beginnings, information theory is closely connected with computer and neuroscience and provides a sound framework to address various problems in these fields. This course will cover the central notions and fundamental results of information theory and then look on applications in machine learning, statistical inference and computer vision, based on selected parts of [1]. The ideal participant of this class would have some background in machine learning or neuroinformatics. She should enjoy mathematical thinking and be prepared to work through formal problems in decent detail. Familiarity with basic calculus, probability theory, and linear algebras does not harm, but is no hard requirements for this course. [1] David MacKay (2005): Information Theory, Inference, and Learning Algorithms Cambridge University Press