Artificial Neural Networks and Machine Learning: Theoretical Foundations


This course will review core mathematical concepts and results that play an important role within the field of machine learning. Various models and architectures of neural networks will be presented, together with their corresponding universal approximation properties. Aspects of learning and generalisation will be addressed within the framework of statistical learning theory. The generality of this theory will be exemplified in the context of neural networks and support vector machines. Further theoretical approaches to learning, in particular gradient-based approaches, will be reviewed. Here, the information-geometric perspective of the natural gradient method will be highlighted.

Date and time info
Thursday, 11:15 - 12:45, first lecture: Nov. 15, 2018

MSc students, PhD students, Postdocs


01.10.18 31.01.19

Regular lectures Winter semester 2018-2019

MPI for Mathematics in the Sciences / University of Leipzig see the lecture detail pages

Katharina Matschke

MPI for Mathematics in the Sciences Contact via Mail