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Fundamentals of Optimization for Machine Learning

CIT413031Elective Modules5 ECTSEnglishUnregelmäßigDepartment Mathematics
AI-edited module sheet. Based on the TUMonline module description, edited for readability.Original in TUMonline

What it is about

You will learn fundamentals and advanced techniques of optimization, both convex and nonconvex as well as combinatorial and continuous, with a focus on applications in machine learning. In the end you will be able to understand optimization problems from ML research and approach research questions in this area.

What you will be able to do

  • Foundations of convex and nonconvex optimization
  • Methods for continuous and combinatorial problems
  • Application of optimization methods in machine learning
  • Preparation for research-oriented questions (e.g., optimization for transformers)

What the module consists of

  • VorlesungProvision of theoretical foundations and research topics
  • Übung/Practice sessionsDeepening and applying the lecture content through exercises

Teaching method

  • Vorlesungento explain concepts and current topics
  • Übungsaufgabento deepen and practice the methods
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Official page in TUMonline · Details are not binding.