Sergey Nikolenko

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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

Machine Learning at the Academic University

This is a year-long course in machine learning presented in 2012 at the St. Petersburg Academic University. See the first part; this is the second semester.

The course itself (all slides and lecture notes are in Russian):

1. Reinforcement learning I. Exploration vs. exploitation. Multiarmed bandits.

Slides (.pdf, 375kb)

2. Reinforcement learning II. Markov decision processes. Value functions, Bellman equations. Policy improvement. Monte Carlo approaches, on-policy and off-policy methods. TD-learning.

Slides (.pdf, 954kb)

3. Reinforcement learning III. Gitting indices; Gittins theorem (with proof). Regret minimization, UCB1 and a logarithmic bound on its regret. Trend following: dynamic Gamma--Poisson.

Slides (.pdf, 882kb)

4. Probabilistic graphical models. Directed graphical models (BBNs), undirected graphical models, and factor graphs. Example: marginalization on a linear chain.

Slides (.pdf, 1107kb)

5. Belief propagation by message passing. The message passing algorithm in the general case (a tree).

Slides (.pdf, 1038kb)

6. Approximate inference. Loopy belief propagation. General form of the EM algorithm.

7. Variational approximations. Properties of factorized approximations.

8. Variational inference II. Variational bound for the univariate Gaussian. Variational bound for a mixture of Gaussians.

9. The Latent Dirichlet Allocation model (LDA). Applications and extensions (talk by Ilya Chernyavsky).

Slides (.pdf, 1916kb)

10. Sampling.

Slides (.pdf, 1097kb)

11. Expectation Propagation: approximate inference for complex factors. Examples.

12. Case study: Bayesian rating models. The Elo rating. Bradley-Terry models and minorization-maximization learning algorithms. The TrueSkill model and its modifications.

Slides (on rating models only, .pdf, 3150kb)

13. Case study: recommender systems.

Slides (.pdf, 1484kb)

14. Deep learning. Products of experts. Contrastive divergence.

15. Reducing dimensionality. Principal components analysis. Probabilistic PCA. Kernel trick and nonlinear PCA.

Slides (.pdf, 398kb)

1. Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer, Information Science and Statistics series, 2006.
2. Trevor Hastie, Robert Tibshirani, and Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed., Springer, 2009.
3. David J. C. MacKay. Information Theory, Inference, and Learning Algorithms. Cambridge University Press, 2003.
4. Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction. MIT Press, Cambridge, MA, 1998.