# Subject description - A4B33RPZ

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 A4B33RPZ Pattern Recognition and Machine Learning Extent of teaching: 2P+2C Guarantors: Roles: PO,V Language ofteaching: CS Teachers: Completion: Z,ZK Responsible Department: 13133 Credits: 6 Semester: Z

Anotation:

The basic formulations of the statistical decision problem are presented. The necessary knowledge about the (statistical) relationship between observations and classes of objects is acquired by learning on the raining set. The course covers both well-established and advanced classifier learning methods, as Perceptron, AdaBoost, Support Vector Machines, and Neural Nets.

Study targets:

To teach the student to formalize statistical decision making problems, to use machine learning techniques and to solve pattern recognition problems with the most popular classifiers (SVM, AdaBoost, neural net, nearest neighbour).

Course outlines:

 1 The pattern recognition problem. Overview of the Course. Basic notions. 2 The Bayesian decision-making problem, i.e. minimization of expected loss. 3 Non-bayesian decision problems. 4 Parameter estimation. The maximum likelihood method. 5 The nearest neighbour classifier. 6 Linear classifiers. Perceptron learning. 7 The Adaboost method. 8 Learning as a quadratic optimization problem. SVM classifiers. 9 Feed-forward neural nets. The backpropagation algorithm. 10 Decision trees. 11 Logistic regression. 12 The EM (Expectation Maximization) algorithm. 13 Sequential decision-making (Wald´s sequential test). 14 Recap.

Exercises outline:

Students solve four or five pattern recognition problems, for instance a simplified version of OCR (optical character recognition), face detection or spam detection using either classical methods or trained classifiers.
 1 Introduction to MATLAB and the STPR toolbox, a simple recognition experiment 2 The Bayes recognition problem 3 Non-bayesian problems I: the Neyman-Pearson problem. 4 Non-bayesian problems II: The minimax problem. 5 Maximum likelihood estimates. 6 Non-parametric estimates, Parzen windows. 7 Linear classifiers, the perceptron algorithm 8 Adaboost 9 Support Vector Machines I 10.Support Vector Machines II 11 EM algoritmus I 12.EM algoritmus II 13 Submission of reports. Discussion of results. 14 Submission of reports. Discussion of results.

Literature:

 1 Duda, Hart, Stork: Pattern Classification, 2001. 2 Bishop: Pattern Recognition and Machine Learning, 2006. 3 Schlesinger, Hlavac: Ten Lectures on Statistical and Structural Pattern Recognition, 2002.

Requirements:

Knowledge of linear algebra, mathematical analysis and probability and statistics.

Webpage:

http://cw.felk.cvut.cz/doku.php/courses/a4b33rpz/start

Keywords:

pattern recognition, statistical decision-making, machine learning, classification

Subject is included into these academic programs:

 Program Branch Role Recommended semester MPBIO1 Biomedical Informatics V – MPBIO2 Biomedical Engineering V – BPKYR1 Robotics V 5 BPKYR_BO Common courses V 5 BPKYR3 Systems and Control V 5 BPKYR2 Sensors and Instrumentation V 5 BPKME1 Communication Technology V 5 BPKME5 Komunikace a elektronika V 5 BPKME_BO Common courses V 5 BPKME4 Network and Information Technology V 5 BPKME3 Applied Electronics V 5 BPKME2 Multimedia Technology V 5 BPEEM1 Applied Electrical Engineering V 5 BPEEM_BO Common courses V 5 BPEEM2 Electrical Engineering and Management V 5 BMI(ECTS) Manager Informatics V – BWM(ECTS) Web and Multimedia V – BIS(ECTS) Intelligent Systems V – BSI(ECTS) Software Engineering V – BPOI2 Computer and Information Science PO 5

 Page updated 21.2.2020 17:51:44, semester: Z,L/2020-1, 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)