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Stochastic Methods

Description

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.

People

 

Horenko I.

Course director

Additional information

Semester
Spring
Academic year
2021-2022
ECTS
6
Language
English
Education
Master of Science in Artificial Intelligence, Foundation course, 1st year
Master of Science in Computational Science, Elective course, 1st year