PATTERN RECOGNITION (Pattern Recognition)

Credits:9
TEACHER:   Giovanni  Gallo


Syllabus:
* The automatic classification problem
* Errors and performance of a classifier
* Elements of Probability Theory
* Bayes formula
* Bayesian classifier
* MAP classifier
* Parametric Data Models and their applications
* Fisher discriminant
* Principal components analysis
* Bayesian networks
* Markov fields
* Hidden Markov Models
* E-M class algorithms 
* Kohonen maps
* Elements of neural networks
* Back-propagation algorithm
* Non parametric models
* Parzen windows
* K-nn, C-means, Fuzzy C-means
* Hierarchical cluestring
* Quinlan algorithm