Monte Carlo Methods

Fri, 06 Nov 2020 00:00:00 +0000

Organisation: Prof. Dr. Verena Wolf, Michael Backenköhler.

Registration

Please use the seminar assignment system to register.

Topic

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.

Date	Topic
7. 12.	Variance Reduction
14. 12.	Random Process Generation
4. 1.	Probabilistic Programming
11. 1.	Markov Chain Monte Carlo
18. 1.	Stochastic Gradient Descent
25. 1.	Rare Event Simulation and Cross-Entropy Method
1. 2.	Evolutionary Algorithms

Organisation

All seminar meetings take place online (no physical meetings).
A kick-off meeting takes place on November 6 at 11:00.
The seminar takes place Mondays at 9:00 sharp. A list of dates and topics can be found above and in the CMS.
The seminar language is English.
The seminar earns you 7 ECTS.

Grade

The final grade consists of: your presentation (50%), reports (40%), and participation during the seminar discussions (10%).

Requirements