Optimization for Machine Learning

This website offers an open and free introductory course on optimization for machine learning. The course is constructed holistically and as self-contained as possible, in order to cover most optimization principles and methods that are relevant for optimization.

This course is recommended as an introductory graduate-level course for Master’s level students.

If you want to learn more about this course, please (1) read the outline further below and (2) read the section on prerequisites

Later on, please note: (1) The course uses a unified mathematical notation. We provide cheat sheets to summarize the most important symbols and concepts. (2) Most sections already contain exercises with worked-out solutions to enable self-study as much as possible.

The course material is developed in a public github repository: https://github.com/slds-lmu/lecture_optimization. You can find the changelog at: https://github.com/compstat-lmu/lecture_i2ml/blob/master/CHANGELOG.md.

If you love teaching ML and have free resources available, please consider joining the team and email us now! (bernd.bischl@stat.uni-muenchen.de or julia.moosbauer@stat-uni-muenchen.de)