Sergey Nikolenko

Sergey Nikolenko

Main page

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
userinfonikolenko (in Russian)

Teaching activities

Machine Learning for CS Club: Kazan 2014

This is a short introduction to machine learning presented as part of the Computer Science Club program in Kazan; see also the course page.

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

1. Introduction. History of AI. Overview of different problem settings in machine learning. Probability theory basics. Bayes theorem and maximal a posteriori hypotheses.
Slides (.pdf, 297kb)
2. Sample application of Bayesian ideas: Laplace's rule of succession. Priors. Conjugate priors. Beta distribution as a conjugate prior for Bernoulli trials. Parametric and nonparametric models: nearest neighbors. Curse of dimensionality.
Slides (.pdf, 714kb)
3. Linear regression. Least squares, polynomial curve fitting. Overfitting: ridge regression. Ridge regression as Gaussian priors. Other kinds of regularizers: lasso regression. Linear classification: logistic regression.
Slides (.pdf, 1673kb)
4. Graphical models. Directed graphical models, d-separation. Undirected graphical models. Factor graphs. Message passing: sum-product on a chain, sum-product on a general tree. Overview of approximate inference algorithms.
Slides (.pdf, 1504kb)
5. The Expectation-Maximization algorithm: mixture of Gaussians for clustering, general case. Hidden Markov models: definitions, the three problems, the Baum--Welch algorithm.
Slides (.pdf, 710kb)
6. Sample applications of probabilistic modeling: text categorization (naive Bayes), topic modeling (LDA), recommender systems (nearest neighbors and SVD), Bayesian rating systems (TrueSkill).
Slides (.pdf, 3391kb)

Selected references.

  1. Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer, Information Science and Statistics series, 2006.
  2. Kevin Murphy. Machine Learning: A Probabilistic Perspective, MIT Press, 2012.
  3. David J. C. MacKay. Information Theory, Inference, and Learning Algorithms. Cambridge University Press, 2003.