Sergey Nikolenko![Sergey Nikolenko](sergey150_3.jpg) Main page
print ' Books';
print ' Research papers';
print ' Talks and posters';
print ' Students';
print ' Popular science';
print ' Other stuff';
print ' Research';
print ' CS and crypto';
print ' Bioinformatics';
print ' Machine learning';
print ' Algebraic geometry';
print ' Algebra';
print ' Bayesian networks';
print ' Earth sciences';
print ' Teaching';
print ' 2014';
print ' ML, KFU';
print ' Game Theory, HSE';
print ' Mech. Design, HSE';
print ' ML, CSClub Kazan';
print ' Game theory, HSE';
print ' Math. logic, AU';
print ' Machine learning, STC';
print ' Machine learning, AU';
print ' 2013';
print ' Discrete math, HSE';
print ' Machine learning, STC';
print ' Math. logic, AU';
print ' Cryptography, AU';
print ' 2012';
print ' Machine learning, STC';
print ' Math. logic, AU';
print ' Machine learning II, AU';
print ' Machine learning, AU';
print ' Machine learning, EMC';
print ' 2011';
print ' Cryptography, AU';
print ' Math. logic, AU';
print ' Machine learning, AU';
print ' 2010';
print ' Math. logic, AU';
print ' Machine learning, AU';
print ' Cryptography, AU';
print ' 2009';
print ' Crypto in CS Club';
print ' Statistics';
print ' Machine learning, AU';
print ' Cryptography';
print ' 2008';
print ' Speech recognition';
print ' MD for CS Club';
print ' ML for CS Club';
print ' Mechanism design';
print ' 2007';
print ' Machine Learning';
print ' Probabilistic learning';
print ' External links';
print ' Google Scholar profile';
print ' DBLP profile';
print ' LiveJournal account
nikolenko (in Russian) | ';
print '![](spacer.gif) | ';
?>
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).
- Slides ()
- 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 ()
- 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)
Selected references
- 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.
|