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CONTENT

UNIDAD 1:          INTRODUCTION (slides)

1.1Frequency distributions.
1.2 Mean, Median, Mode and other statistical moments
1.3 Standard deviation and similar measures
1.4 Skweness and Kurtosis
1.5 Basics on probability
1.6 Continuos and discrete random variables
1.7 Random vectors

UNIDAD 2:          BAYESIAN DECISION THEORY AND SOME TECHNIQUES FOR CLASSIFICATION(slides)

 2.1 Discriminant functions and decision surfaces
2.2 Bayesian classification for Gaussian distributions
2.3 Maximum-Likelihood estimation
2.4 Bayesian estimation
2.4 Expectation-Maximization
2.5 Density estimation
2.6 Parzen windows
2.7 K-nearest neighbor classifier
2.8 Fuzzy classification
2.9 Bayesian networks
2.10 Hidden Markov models

UNIDAD 3:         LINEAR CLASSIFIERS (slides)

3.1 Linear discriminant
3.2 The perceptron
3.3 Support Vector Machine

UNIDAD 4:        PRACTICAL TIPS (slides)

4.1 Feature selection
4.2 Reduction of dimensionality
4.3 Data tranformation

BIBLIOGRAPHY

1. Richard O. Duda, Peter E. Hart and David G. Stork, Pattern Classification. 2ed, Ed. Wiley-Interscience, 2001.

2. Theodoridis S, Koutroumbas K, Pattern Recognition. 4ed, Ed. AP, 2009.

3. Christopher m. Bishop, Pattern Recognition and Machine Learning. Ed. Springer, 2006.