Pattern Recognition

The course is not on the list Without time-table

Code	Completion	Credits	Range	Language
XP33ROD	ZK	4	2P+2S	Czech

Course guarantor:

Lecturer:

Tutor:

Supervisor:

Department of Cybernetics

Synopsis:

See https://cw.fel.cvut.cz/wiki/courses/xp33rod/start

Requirements:

I assume the student has basic mathematical background. Being familiar with probability theory and statistics is a plus.

Syllabus of lectures:

Formulation of the basic tasks solved in pattern recognition. Bayesian and non-Bayesian tasks.

Two special useful statistical models. Conditional independence of features. Gaussian models. Strightening of the feature space.

Estimation of probabilistic models. Parametric and nonparametric methods.

Experimental evaluation of classifiers. Receiver operator curve (ROC).

Learning in pattern recognition. VC dimension. Estimate of the needed length of the training sequence.

Linear classifier. SVM classifier. Kernel methods.

Convolutional neural networks.

Unsupervised learning. Cluster analysis. EM (Expectation Maximization) algorithm.

Intro to structural methods embedded into the statistical framework. Recognition of Markovian sequences.

Structural pattern recognition, a classical approach.

Experiences learned from practial implementations of pattern recognition methods.

Syllabus of tutorials:

The subject does not have labs or exercises. Students may write a training paper with the help of the lecturer.

Study Objective:

Study materials:

Schlesinger M.I., Hlavac V.: Ten lectures from statistical and structural

pattern recognition, Kluwer Academic Publishers, 2002.

Duda R.O., Hart P.E., Stork D.G.: Pattern Classification, John Wiley and

Sons, 2001.

Bishop, C.M: Pattern Recognition and Machine Learning, Springer, New York, 2006.

Note:

Further information:

https://cw.fel.cvut.cz/wiki/courses/xp33rod/start

No time-table has been prepared for this course

The course is a part of the following study plans: