Stochastic Methods

COURSE OBJECTIVES

To get unified mathematical, algorithmic, and numerical perspectives on different stochastic methods from AI and CS, get practical experience with running and comparing algorithms on a practical data analysis problem.

COURSE DESCRIPTION

Many of the real-life applications (e.g., in banking/insurance, mechanics, medicine, etc.) can be only approached, modeled, and computed as stochastic (or random) processes. The aim of this course is to introduce the most essential mathematical concepts and computational methods from the area of stochastic and random processes and algorithms. The recurrent theme of the course is in establishing a joint stochastic/statistic perspective based on optimization paradigm - for various computational methods and algorithms from computational science, machine learning, and informatics.

LEARNING METHODS

In-class lectures and exercises, audio-annotated lecture slides are provided.

EXAMINATION INFORMATION
Written exam

REFERENCES

• Handbook of Stochastic Methods: for Physics, Chemistry and the Natural Sciences; C. Gardiner, 2004.