Sergey Nikolenko
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Research';
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CS and crypto';
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Bioinformatics';
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Machine learning';
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Algebraic geometry';
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Algebra';
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Bayesian networks';
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Earth sciences';
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Teaching';
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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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External links';
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Google Scholar profile';
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DBLP profile';
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LiveJournal account
nikolenko (in Russian)
Teaching activities
Machine Learning
In the fall of 2006 I'm teaching the "Machine Learning" course. In spring I delivered an introductory lecture on machine learning and fuzzy logic, briefly describing what I plan to cover. The slides are available here (.pdf, 598kb, in Russian).The course itself (all slides and lecture notes are in Russian):
- 1. Decision trees.
- Lecture notes (.pdf, 390kb)
Slides ()
ID3 in Python and Ruby (Test input, Test output) - 2. Artificial Neural Networks I.
- Lecture notes (.pdf, 363kb)
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Perceptron training in Python and Ruby. - 3. Artificial Neural Networks II.
- Lecture notes (.pdf, 331kb)
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Gradient descent in Python. - 4. Genetic algorithms.
- Lecture notes (.pdf, 386kb)
Slides () - 5. Genetic programming and a bit of practice.
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- 6. Bayesian learning. Naive Bayes classifier.
- Lecture notes (.pdf, 347kb)
- Slides ()
- 7. Bayesian learning II. Concept learning.
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- 8. Bayesian belief networks. Basic notions and propagation in polytrees.
- 9. Bayesian belief networks II. Moral graphs, junction trees, propagation.
- 10. Learning Bayesian networks. EM algorithm.
- 11. Clustering. Graph algorithms, hierarchical clustering, FOREL.
- 9. Bayesian belief networks II. Moral graphs, junction trees, propagation.
- Slides ()
- 12. Clustering II. EM for clustering, k-means, fuzzy c-means.
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- 13. Reinforcement learning.
- Lecture notes (.pdf, 414kb)
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