Sergey NikolenkoMain page Books Research papers Talks and posters Students Popular science Other stuff Research CS and crypto Bioinformatics Machine learning Algebraic geometry Algebra Bayesian networks Earth sciences Teaching 2014 ML, KFU Game Theory, HSE Mech. Design, HSE ML, CSClub Kazan Game theory, HSE Math. logic, AU Machine learning, STC Machine learning, AU 2013 Discrete math, HSE Machine learning, STC Math. logic, AU Cryptography, AU 2012 Machine learning, STC Math. logic, AU Machine learning II, AU Machine learning, AU Machine learning, EMC 2011 Cryptography, AU Math. logic, AU Machine learning, AU 2010 Math. logic, AU Machine learning, AU Cryptography, AU 2009 Crypto in CS Club Statistics Machine learning, AU Cryptography 2008 Speech recognition MD for CS Club ML for CS Club Mechanism design 2007 Machine Learning Probabilistic learning External links Google Scholar profile DBLP profile LiveJournal account nikolenko (in Russian)  
Teaching activities 
Probabilistic Learning
In the spring of 2007 I'm teaching the "Probabilistic Learning" course.
The course is coupled with a seminar, and the seminar materials will also
appear here.
The course materials (all slides and lecture notes are in Russian):
 1. Introduction. Probability theory and Bayesian inference.
 Slides (.pdf, 642kb)
 2. Bayesian inference by brute force. Exact marginalization by integration.
 Slides (.pdf, 630kb)
 3. Bayesian inference and coding theory. Error correcting as MAP hypothesis search.
 Slides (.pdf, 391kb)
 4. General sumproduct algorithm for marginalization. Laplace approximations.
 Slides (.pdf, 451kb)
 5. Monte–Carlo methods: sampling. Markov chains, MCMC, Metropolis–Hastings, slice sampling.
 Slides (.pdf, 551kb)
 6. Hidden Markov models. Introduction, main tasks, their solutions. Baum–Welch algorithm.
 Slides (.pdf, 455kb)
 7. Hidden Markov models II. HMM with continuous observables, autoregressive models, HMM comparison, MMI and MDI criteria.
 Slides (.pdf, 478kb)
 8. Bayesian approach to neural networks. Adapting learning speed. Learning neural networks as Bayesian inference. Gaussian processes and splines.
 Slides (.pdf, 712kb)
 9. Hopfield networks.
 Slides (.pdf, 343kb)
Selected references
 David J. C. MacKay. Information Theory, Inference, and Learning Algorithms. Cambridge University Press, 2003.
