Bayesian Model Selection


The course is dedicated to the basis of Bayesian Methods in Machine Learning, Bayesian Models’ construction and inference. The notion of optimal bayesian forecast is introduced, and the methods for constructing the one are studied. The methods for handling non-linearities and inhomogeneities in data are discussed. Handling of missing data and optimal model evolution with time are considered from a Bayesian perspective.



4 theoretical tasks (performed individually), 1 practical task and 1 competition (performed in teams), 2 tests. Written and oral exam (the latter can be substituted by the presentation on a selected topic).


Each theoretical task gives at most 50 / 100 points, while practical one and competition each give up to 50 points. The score for the TOP1 student in each task is doubled (extra points). Written and oral exams add up to 250 together. The final mark is computed by scaling the total score by maximal one assuming no extra points gained.


Probability, Basic Machine Learning, Linear Algebra, Optimization.