Subject description - XP33ROD

Summary of Study | Summary of Branches | All Subject Groups | All Subjects | List of Roles | Explanatory Notes               Instructions
XP33ROD Pattern Recognition Extent of teaching:2P+2S
Guarantors:Hlaváč V. Roles:S Language of
teaching:
CS
Teachers:Hlaváč V. Completion:ZK
Responsible Department:13133 Credits:4 Semester:L

Anotation:

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

Course outlines:

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. Learning in pattern recognition. VC dimension. Estimate of the needed length of the training sequence. Linear classifier. SVM classifier. Kernel methods. 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.

Exercises outline:

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

Literature:

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.

Requirements:

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

Webpage:

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

Keywords:

statistical pattern recognition, machine learning, Bayes classier, classifier learning, SVM, unsupervied learning

Subject is included into these academic programs:

Program Branch Role Recommended semester
DOKP Common courses S
DOKK Common courses S


Page updated 6.12.2019 17:52:32, semester: Z,L/2020-1, L/2018-9, Z,L/2019-20, Send comments about the content to the Administrators of the Academic Programs Proposal and Realization: I. Halaška (K336), J. Novák (K336)