Continuous Optimization for Artificial Intelligence
Kontinuierliche Optimierung für Künstliche Intelligenz
6 CPirregularlyEN
Prerequisites
Recommended, as written
Knowledge of the content of the module • 14015 Introduction to Mathematical Methods in Artificial Intelligence
Contents
Topics covered include: • first-order optimisation (gradient descent, adaptive moment estimation [ADAM]), • second-order optimization (Newton/quasi-Newton), as well as • Karush-Kuhn-Tucker conditions, • alternating direction method of multipliers [ADMM], • sparsity, and • duality.
Learning outcomes
Upon successful completion of the module, students have gained in-depth knowledge of continuous optimization. They have a theoretical understanding of methods and algorithms. In addition, students are able to analyze and tackle relevant problems.
Assessment
• Written examination, 90 min.
Literature (3)
- M. P. Deisenroth, A. A. Faisal, and C. S. Ong: Mathematics for Machine Learning, Published by Cambridge University Press, 2020.
- S. Sra, S. Nowozin, S.J. Wright: Optimization for Machine Learning, The MIT Press, 2012.
- S. Boyd, L. Vandenberghe: Convex Optimization, Cambridge University Press, 2004.
Remarks
• Study programme Artificial Intelligence M.Sc.: Compulsory elective module in complex „Learning and Reasoning" • Study programme Mathematical Data Science M.Sc.: Compulsory elective module in complex „Fundamentals of Data Science"
Source: BTU's module description · retrieved 29 Sep 2026Original