## EE 595 Independent Studies On## Statistical Learning## Fall 2005 |

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**Dr.
Sen-ching Cheung** (cheung@engr.uky.edu)

Offices: 687B FPAT (7-9113)

Office hours: MWF,

Statistical learning theory
is playing an increasingly important role in diverse areas such as communication,
bioinformatics, computer vision, neuroscience, and economics. The goals of this
course are twofold: first, provide a solid foundation on the fundamental
concepts in statistical learning theory so that graduate students can apply
them in their own research; second, discuss recent results and new applications
in this area so as to stimulate new research ideas. The course will meet three
times a week, discussing papers, book chapters, and homework problems. The
meetings will tentatively be held MWF 4:30-5:30pm at MIA Lab in the
Visualization Center.

- Graphical model, sum-product and junction tree
algorithms
- Linear and generalized linear models
- Exponential family, sufficiency, conjugacy
- Density estimation, kernel methods, mixture
models
- Expectation-Maximization algorithm for parameter
estimation
- Hidden Markov models (HMM)
- Factor analysis, principal component analysis
(PCA), canonical correlation
analysis (CCA) and independent component analysis (ICA)
- Kalman filtering
- Approximate inferencing I: Markov-chain
Monte-Carlo (MCMC) and particle filtering
- Approximate inferencing II: mean-field, loopy
belief propagation
- Model selection, marginal likelihood, AIC, BIC
and MDL
- Vapnik Chervonenkis theory and risk bounds
- Kernel methods and Support Vector Machine (SVM)
- Ensemble methods: bagging and boosting
- Nonparametric Bayes, Dirichlet processes
- Decision networks, Markov decision processes and
reinforcement learning

Grades will be assigned based on participation (50%)
and a substantial final project (50%).
Titles and scopes of final projects will be jointly determined by the
instructor and students. Each topic should involve substantial amount of
LITERATURE SURVEY AND EXPERIMENTAL RESULTS. The prerequisite are basic
statistics, probability and stochastic systems.

No text required. Copies of
papers and book chapters will be provided. The following books are recommended:

- The Elements of Statistical Learning by T. Hastie
et al.
- All of Statistics by L. Wasserman
- The Nature of Statistical Learning Theory by V.
Vapnik.
- Learning with Kernels by B. Schlkopf and A. J.
Smola
- Probabilistic Networks and Expert Systems by R.
G. Cowell et al.
- Independent Component Analysis by A. Hyvarinen et
al.
- Pattern Recognition by S. Theodoridis and K.
Koutroumbas
- Artificial Intelligence: A Modern Approach by S.
Russell and P. Norvig
- Machine Learning by T. Mitchell.

Sen-ching Samson Cheung Last modified: Tue Aug 24 11:20:57 EDT 2004