FS2022: 62890 Seminar Advanced Topics in Learning and Decision Making

The course starts with a few introductory lectures, before moving on to some advanced concepts. These will be synchronised with Friday's lectures in the basic course. After the introductory lectures are complete, we will have weekly readings of papers. All students will read the paper for the week, and submit paper reviews, summarising the main points, the weaknesses and strengths of each paper, as well as any open problems. The paper will then be presented by one of the students, and the remaining students will ask questions.

- Complexity of Markov decision processes
- Stochastic approximation and reinforcement learning
- Function approximation
- PAC bounds
- Regret bounds

Highly motivated students that will do a MSc thesis in the field, or that may later wish to do a PhD.

Previous exposure to probability, linear algebra necessary.
A previous course in machine learning, and in particular reinforcement learning is strongly recommended.
It is highly recommended to follow the course "Reinforcement Learning and Decision Making Under Uncertainty". The paper sets to be discussed will be aligned with the lecture scheduled in the other course.

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