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Technische Universität München, Department of Mathematics


Optimization Methods for Machine Learning (Modern Methods in Nonlinear Optimization)

Prof. Dr. Michael Ulbrich

Summer Term 2017

Contents - News - Dates - Lecture Notes - Exercises - Exam - Literature


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.

Module number: MA4503. The module description can be found here.

Prerequisites: MA2503 (Nichtlineare Optimierung: Grundlagen), MA3503 (Nonlinear Optimization: Advanced)



  Monday 14:15 - 15:45 019, LMU Hörsaal im Physik Werkstattgebäude Prof. Dr. Michael Ulbrich    

Exercises (biweekly)
Group 1 Monday 12:15 - 13:45 MI 02.04.011 Sebastian Garreis/Philipp Jarde next date: ---  
Group 2 Tuesday 10:15 - 11:45 MI 02.08.020 Sebastian Garreis/Philipp Jarde next date: ---  
Group 3 Wednesday 12:30 - 14:00 MI 03.08.011 Sebastian Garreis/Philipp Jarde next date: ---  

Lecture Notes

A current version of the lecture notes is available for download (last update: July 26th, 2017).
The lecture notes will be dynamically updated as the course proceeds.



Main exam Friday, August 4th, 2017 14:00 - 15:00 MW 0001
Resit exam Friday, October 6th, 2017 11:00 - 12:00 MW 1801

Important information (about the main exam):

Inspection (Klausureinsicht):

Information about the resit exam:


This list will be updated as the course proceeds.