Machine Learning
The newest version of the course on wiki
MachineLearning.ru
(in Russian only).
Semester 1:

Introduction
(18.10.2007):
basic notions of learning by examples,
examples of applied problems,
empirical risk minimization,
maximization of likelihood,
overfitting,
crossvalidation.

Bayesian theory of classification
(16.04.2008):
the optimality of Bayesian classifier,
nonparametric density estimations,
Parzen window,
quadratic discriminant,
linear discriminant of Fisher,
the mixture model of density,
EMalgorithm,
the network of radial basis functions.

Similaritybased classification
(16.04.2008):
nearest neighbor classifier and its generalization,
Parzen window again,
potential functions method,
object weights optimization,
objects filtering.

Clustering
(22.11.2007):
graphs approaches,
statistical clustering (EM and kmeans),
agglomerative algorithm, dendrogram.

Regression
(21.12.2007):
nonparametric regression,
robust nonparametric regression (LOWESS),
linear regression,
singular value decomposition,
regularization and ridge regression,
nonlinear regression,
logistic regression,
generalized additive models,
orthogonalization and stepwise regression,
lasso and least angle regression.

Generalization and model assessment
(13.12.2006):
crossvalidation,
VapnikChervonenkis theory,
overfitting,
structural risk minimization,
Akaike information criterion,
Bayes information criterion.

Features selection
(21.12.2007):
internal and external criteria,
complexity optimization concepts,
adddel and stepwise methods,
depthfirst and breadthfirst search strategies,
iterative procedure of GMDH (group method of data handling),
genetic features selection,
adaptive stochastic search.
Semester 2:

Neural netwirks
(18.10.2007):
onelayer perceptron,
Rozenblatt's, Hebb's, delta (ADALINE) rules,
Novikov's theorem,
stochastic gradient,
multilayer perceptron,
backpropagation,
weight decay,
optimal brain damage,
many heuristics against overfitting, paralysis and slow convergence.
Kohonen network,
WTA, WTM and CWTA strategies,
Kohonen maps and the art of their interpreting.

Support Vector Machine
(18.10.2007):
optimal separating hyperplane,
hard and softmargin hyperplane,
kernel trick,
ten rules to build kernels,
links with twolayer networks, RBF and EMalgorithm.

Compositions
(22.11.2007):
simple, majority, seniority and weighted voting,
heuristic algorithms for learning simple and seniority voting compositions,
boosting, bagging, random subspace method,
mixture of experts,
hierarchical mixture of experts.

Logic (discrete) classification
(21.12.2007):
basic notions of predicate, rule and regularity,
informativity criteria,
forms of regularities (conjunctions, balls? and hyperplaines in lowdimension subspaces),
stochastic local search for learning conjunctions,
decision list,
decision tree, ID3, prepruning and postpruning,
lookahead and anytime algorithms,
depthfirst and breadthfirst search strategies (KORA and TEMP algorithms),
weighted voting of rules via boosting,
Zhuravlev's algorithm of estimations calculation,
association rules and aPriory algorithm.
The course programme (in Russian)
Practical work (in Russian)
Scientific research directions
Full text here:
MachineLearning.ru
(in Russian).

Bounding the probability of overfitting,
enhancement the generalization ability of learning algorithms,
VapnikChervonenkis theory,
computational learning theory,
shell bounds.

Combinatorial statistics,
exact statistical tests,
nonparametric statistics.

Multiple classifier systems,
ensemble learning,
classifier fusion,
mixture of experts.

Collaborative filtering and clients environment analysis,
web usage mining,
personalization,
client relationship management.