Sergey Nikolenko
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2014';
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ML, KFU';
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Game Theory, HSE';
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Mech. Design, HSE';
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ML, CSClub Kazan';
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Game theory, HSE';
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Math. logic, AU';
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Machine learning, STC';
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Machine learning, AU';
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2013';
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Discrete math, HSE';
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Machine learning, STC';
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Math. logic, AU';
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Cryptography, AU';
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2012';
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Machine learning, STC';
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Math. logic, AU';
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Machine learning II, AU';
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Machine learning, AU';
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Machine learning, EMC';
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2011';
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Cryptography, AU';
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Math. logic, AU';
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Machine learning, AU';
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2010';
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Math. logic, AU';
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Machine learning, AU';
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Cryptography, AU';
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2009';
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Crypto in CS Club';
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Statistics';
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Machine learning, AU';
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Cryptography';
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2008';
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Speech recognition';
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MD for CS Club';
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ML for CS Club';
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Mechanism design';
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2007';
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Machine Learning';
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Probabilistic learning';
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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 ()
- 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 ()
- 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 ()
- 4. Probabilistic graphical models. Directed graphical models (BBNs), undirected graphical models, and factor graphs. Example: marginalization on a linear chain.
- Slides ()
- 5. Belief propagation by message passing. The message passing algorithm in the general case (a tree).
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- 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).
- 7. Variational approximations. Properties of factorized approximations.
- Slides ()
- 10. Sampling.
- Slides ()
- 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, )
- 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)
- Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer, Information Science and Statistics series, 2006.
- Trevor Hastie, Robert Tibshirani, and Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed., Springer, 2009.
- David J. C. MacKay. Information Theory, Inference, and Learning Algorithms. Cambridge University Press, 2003.
- Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction. MIT Press, Cambridge, MA, 1998.