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Optimization for Machine Learning
This course teaches an overview of modern mathematical optimization methods for machine learning and data science applications. In particular, the scalability of algorithms to large datasets will be discussed both theoretically and in practice.
This advanced lecture aims to prepare students to research this topic. An interest in and the ability to understand and apply mathematical proofs are essential.
Learning Prerequisites
- Previous coursework in calculus, linear algebra, and probability is required.
- Familiarity with optimization and/or machine learning is beneficial.
Students are recommended to register for this course only as master's students, but attendance is also possible for bachelor students in their last semester. There are no strict rules or regulations, but the students must acquire the missing fundamentals independently.
Course Information 2026
- The course will be organized in a block format during the summer break, providing an intensive learning experience. Mornings will be dedicated to lectures, where you will explore theoretical insights and innovative strategies. Afternoons will focus on practical exercises and project work, allowing you to apply what you have learned and deepen your understanding through hands-on experience.
- The course will (tentatively) take place between August 24 and September 4. It will include approximately 10 lectures and exercise sessions, spread across the two weeks, as well as project work and a final project presentation.
- Please note that attendance of at least 80% of the lectures is mandatory.
- Exam: TBA (likely Sep 17-18)