Disciplined Machine Learning

• Organized by: Hao Zhu and Joschka Boedecker
  
• Course number: 11LE13V-1422
• Language: English
• Location: Building 101, SR 00-017/19
• Dates: Wednesdays and Thursdays 2:00–4:00 PM. First lecture on October 21.
• Course material (slides, lecture recordings, exercises and solutions, etc.)  and up to date information will be posted on ILIAS and this page.

Overview

We concentrate on formulating, analyzing, and solving inverse optimization problems that arise in machine learning, with a focus on disciplined convex optimization, and several other typical nonconvex optimization problems. Convex sets, functions, and optimization problems. Constructive convex analysis and disciplined convex programming. Relaxation and duality. Applications to machine learning, signal processing, statistics, control, and finance.

The textbook is Disciplined Machine Learning written by Hao Zhu and Joschka Boedecker, available online.

Requirements

• Exercise sheets have to be completed and handed in on time. To successfully complete the course work (i.e., Studienleistung), you need to have reached at least 50% of the overall number of achievable points for the semester.
• The final exam is a 120 minutes open-book written exam. The exact date and time will be announced later.