Delivered at:: Joint Department with Sberbank ‘Financial Technologies and Data Analysis’

Course type:: Compulsory course

When:: 2 year, 1 module

Abstract

The Bayesian probability allows us to effectively take into account different user preferences when constructing decisive prediction rules. In addition, it allows solving the problems of selecting structural parameters of the model. In particular, it allows solving the problems of feature selection, dimensionality of the reduced space at dimensionality reduction, and values of regularization coefficients without combinatorial search. An important part of the course is the study of modern generative models that use the Bayesian approach.

Learning Objectives

Forming knowledge, skills and abilities in working with complex probabilistic models that take into account the machine learning application structure, to derive the necessary formulas for solving learning and inference problems within the framework of the constructed probabilistic models, as well as to effectively implement them.

Expected Learning Outcomes

to be able to operate complex probabilistic models
to master generative modeling modern approaches

Course Contents

Bayesian probability
Bayesian probability model-building
Sampling methods and generative models

Assessment Elements

Practical assignment 1
Practical assignment 2
Exam
Students are permitted to use any materials during preparation for their answer. Students are not allowed to use anything during their answer (including their written down preparation notes). During the exam students have one hour to prepare their answer to the questions in the exam papers, after which they are to answer the examiner questions from the theoretical minimum, answer to the questions in the exam papers and additional questions about the course and solve the problems.

Interim Assessment

2024/2025 1st module
0.25 * Exam + 0.375 * Practical assignment 1 + 0.375 * Practical assignment 2

Bibliography

Recommended Core Bibliography

Barber, D. (2012). Bayesian Reasoning and Machine Learning. Cambridge: Cambridge eText. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=432721
Deep learning, Goodfellow, I., 2016
Pattern recognition and machine learning, Bishop, C. M., 2006

Recommended Additional Bibliography

Tipping, M. E. (2001). Sparse Bayesian Learning and the Relevance Vector Machine. Journal of Machine Learning Research, 1(3), 211–244. https://doi.org/10.1162/15324430152748236

Authors

Фиалкова Мария Алексеевна

Master’s Programme 'Financial Technologies and Data Analysis'

Contacts

Bayesian Methods for Data Analysis

Course Syllabus