Sergey Nikolenko

Books
Research papers
Talks and posters
Students
Popular science
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

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 (.pdf, 686kb)
ID3 in Python and Ruby (Test input, Test output)
2. Artificial Neural Networks I.: Lecture notes (.pdf, 363kb)
Slides (.pdf, 391kb)
Perceptron training in Python and Ruby.
3. Artificial Neural Networks II.: Lecture notes (.pdf, 331kb)
Slides (.pdf, 539kb)
Gradient descent in Python.
4. Genetic algorithms.: Lecture notes (.pdf, 386kb)
Slides (.pdf, 495kb)
5. Genetic programming and a bit of practice.: Slides (.pdf, 940kb)
6. Bayesian learning. Naive Bayes classifier.: Lecture notes (.pdf, 347kb); Slides (.pdf, 463kb)
7. Bayesian learning II. Concept learning.: Slides (.pdf, 565kb)
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.: Slides (.pdf, 460kb)
12. Clustering II. EM for clustering, k-means, fuzzy c-means.: Slides (.pdf, 504kb)
13. Reinforcement learning.: Lecture notes (.pdf, 414kb)
Slides (.pdf, 578kb)

Selected references.

A.L. Tulupyev, S.I. Nikolenko, A.V. Sirotkin. Bayesian Networks: A Probabilistic Logic Approach. St.-Petersburg, Nauka, 2006. (two first pages of the book: .pdf, 815kb, in Russian, ozon.ru)