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Stochastic Methods


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. Besides of gaining theoretical and practical backgrounds in the areas of stochastic calculus, random processes and uncertainty quantification, the participants will gain practical skills by doing supervised short research projects from real-life applications. 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.




  • Handbook of Stochastic Methods: for Physics, Chemistry and the Natural Sciences; C. Gardiner, 2004;
  • Principles of Data Mining; D. Hand, H. Mannila and P. Smyth; A Bradford Book, 2001



Horenko I.

Course director

Kaiser O.

Course director

Marchenko G.


Additional information

Academic year
Master of Science in Artificial Intelligence, Core course, Lecture, 1st and 2nd year

Master of Science in Computational Science, Elective course, Lecture, 1st and 2nd year

PhD programme of the Faculty of Informatics, Elective course, Lecture, 1st, 2nd and 3rd year