Sergey Nikolenko

Sergey Nikolenko

Main page

Research papers
Talks and posters
Popular science
Other stuff

CS and crypto
Machine learning
Algebraic geometry
Bayesian networks
Earth sciences

Game Theory, HSE
Mech. Design, HSE
ML, CSClub Kazan
Game theory, HSE
Math. logic, AU
Machine learning, STC
Machine learning, AU
Discrete math, HSE
Machine learning, STC
Math. logic, AU
Cryptography, AU
Machine learning, STC
Math. logic, AU
Machine learning II, AU
Machine learning, AU
Machine learning, EMC
Cryptography, AU
Math. logic, AU
Machine learning, AU
Math. logic, AU
Machine learning, AU
Cryptography, AU
Crypto in CS Club
Machine learning, AU
Speech recognition
MD for CS Club
ML for CS Club
Mechanism design
Machine Learning
Probabilistic learning

  External links
Google Scholar profile
DBLP profile
LiveJournal account
userinfonikolenko (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 sum-product 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
  1. David J. C. MacKay. Information Theory, Inference, and Learning Algorithms. Cambridge University Press, 2003.