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Technische Universität München, Zentrum Mathematik


Modern Methods in Nonlinear Optimization - Optimization in Machine Learning

Prof. Dr. Michael Ulbrich

Summer term 2021

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Course Content

Machine learning has become a highly important field of research, especially in the context of big data. Many models in supervised learning such as support vector machines or neural networks require training based on data, which calls for suitable nonlinear optimization techniques. This course gives an introduction to modern optimization methods that are well-suited for machine learning tasks. In particular, they a) take into account the specific problem structure that arises in empirical risk minimization, b) are compatible with the results of statistical learning theory, and c) are designed to handle huge amounts of data efficiently. Numerical aspects and illustrative examples will also be part of the lecture.

A table of contents of the lecture:


01.04.2021 Welcome to the course Modern Methods in Nonlinear Optimization - Optimization in Machine Learning. Detailled information on the lecture and exercises will be posted as well in the Moodle course.



The lecture is held on Thursdays, 10:15-11:45 on Zoom. The Zoom link can be found on the Moodle page.