Next semester we will offer the following seminar:
The core idea of Monte Carlo (MC) methods is to perform computer simulations of a real-world system based on pseudo-random numbers. MC is applied in a large variety of research areas: physical sciences, computer sciences, engineering, statistics, finance, etc. Moreover, ideas that have originally been developed in the context of MC are meanwhile also in use for sampling problems in the area of machine learning. Although, being a simple and very direct approach to analyze a system, MC methods come with a lot of challenges, in particular, when sampling rare events or when systems have multiple time scales. In this seminar, we take a computer science perspective on Monte Carlo and cover different MC algorithms to tackle these challenges.
More information can be found on the course page.