This course offers a mathematically rigorous introduction to Bayesian statistics and its related fields. We cover the concepts of prior distributions and evidence, the EM algorithm, MCMC sampling, and Bayesian optimization, along with mathematical foundations.
- Alexander Aduenko Alexander-Aduenko
- Konstantin Yakovlev Konstantin-Iakovlev
| Week | Date | Topic |
|---|---|---|
| 1 | 20260915 | Introduction |
| 2 | 20260922 | Introduction: Naive Bayes Classifier. Exponential Family of distributions |
| 3 | 20260929 | Bayesian Linear Regression and Evidence |
| 4 | 20261011 | Bayesian Linear Regression and Evidence (continued) |
| 5 | 20261013 | Evidence and Feature Selection in Linear and Logistic Regression |
| 6 | 20261020 | Evidence and Feature Selection in Linear and Logistic Regression (continued) |
| 7 | 20261027 | Bayesian Logistic Regression. EM Algorithm |
| Assignment | Published | Deadline |
|---|---|---|
| Theory 1 | 20260926 | 20261011, 16:00 MSK |
| Theory 2 | 20261013 | 20261031, 23:59 MSK |
| Applied 1 | 20261018 | 20261108, 23:59 MSK |
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Barber D. Bayesian reasoning and machine learning. – Cambridge, UK : Cambridge University Press, 2012. – Т. 1.
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Sinho Chewi (2026). Log-Concave Sampling: pdf.