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 at EMC

This is a course in machine learning presented in 2012 at the St. Petersburg office of EMC. The course is very similar to this one but goes a bit further; this page contains the slides for EMC presentations not included in the AU course (all slides are in Russian).

1. Prior distributions. Conjugate priors.
Slides (.pdf, 1025kb)
2. Graphical models and the message passing algorithm. Approximate inference.
Slides (.pdf, 1844kb)
3. Bayesian rating models, TrueSkill.
Slides (.pdf, 3118kb)
4. Latent Dirichlet Allocation. User behaviour models for click logs.
Slides (.pdf, 1360kb)
5. Sampling.
Slides (.pdf, 916kb)
6. Reinforcement learning.
Slides (.pdf, 1596kb)
7. Recommender systems.
Slides (.pdf, 1233kb)
Selected references
  1. Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer, Information Science and Statistics series, 2006.
  2. Trevor Hastie, Robert Tibshirani, and Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed., Springer, 2009.
  3. David J. C. MacKay. Information Theory, Inference, and Learning Algorithms. Cambridge University Press, 2003.