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Summer Block Course Optimization for Machine Learning (Start: August 26)Geschrieben am 07.08.26 von Sebastian Stich Dear students, Welcome to the summer block course on Optimization for Machine Learning. The course will take place over the two-week period from August 24 to September 4, followed by the exam at a later date. On a typical course day, we will have a lecture in the morning, followed by an… Weiterlesen Dear students, Welcome to the summer block course on Optimization for Machine Learning. The course will take place over the two-week period from August 24 to September 4, followed by the exam at a later date. On a typical course day, we will have a lecture in the morning, followed by an exercise session in the afternoon, with additional time for solving exercises and working on the mini-project. Since the course is very condensed, it is important that you set aside enough time over these two weeks. I will be traveling on August 24 and 25, so the first two lectures will be offered asynchronously. I have already uploaded the slides and video recordings, and we will also upload the corresponding exercises. In principle, you can work through these two lectures on August 24 and 25, but you are also welcome to start earlier. In particular, if concepts such as convex sets or gradient descent are new to you, I strongly recommend going through the material in advance so that everyone starts the in-person part of the course with a similar background. The first in-person lecture will therefore take place on August 26, followed by the afternoon exercise session (we will also explain the course modalities in more detail during this first session). |
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.
Dates
| Lecture | Date | Lecture Time | Exercise Time | Notes |
|---|---|---|---|---|
| 1-2 | - | - | - | asynchronously |
| 3 | August 26 | TBA (morning) | TBA (afternoon) | Extra: Lecture 1+2 Q&A Session (after lecture 3) |
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 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 (October 2026)