Stochastic Methods
People
Horenko I.
Course director
Assistant
Description
COURSE OBJECTIVES
To get unified mathematical, algorithmical and numerical perspectives on different stochastic methods from AI and CS, get a practical experience with running and comparing algorithms on practical data analysis problem.
COURSE DESCRIPTION
Many of the real-life applications (e.g., in banking/insurance, mechanics, medicine, etc.) can be only approached, modelled 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 optimisation 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.
Education
- Master of Science in Artificial Intelligence, Core course, Lecture, 1st year
- Master of Science in Computational Science, Core course, Lecture, 1st year
