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XP33ROD	Pattern Recognition
Roles:	S	Extent of teaching:	2P+2S
Department:	13133	Language of teaching:	CS
Guarantors:	Hlaváč V.	Completion:	ZK
Lecturers:	Hlaváč V., Škoviera R.	Credits:	4
Tutors:	Hlaváč V., Škoviera R.	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. 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.

Exercises outline:

The subject does not have labs or exercises. Students may 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. Bishop, C.M: Pattern Recognition and Machine Learning, Springer, New York, 2006.

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	–

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Page updated 26.2.2021 17:52:17, semester: Z/2020-1, L/2021-2, L/2020-1, Z/2021-2, Send comments about the content to the Administrators of the Academic Programs

Proposal and Realization: I. Halaška (K336), J. Novák (K336)