CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2019/2020

# Recognition and machine learning

Code Completion Credits Range Language
B4B33RPZ Z,ZK 6 2P+2C Czech
Corequisite:
The course cannot be taken simultaneously with:
Pattern Recognition and Machine Learning (AE4B33RPZ)
Pattern Recognition and Machine Learning (BE5B33RPZ)
Lecturer:
Ondřej Drbohlav, Jiří Matas (guarantor)
Tutor:
Jiří Matas (guarantor), Filip Naiser, Michal Neoral, Jonáš Šerých, Jan Šochman, Radim Špetlík, Milan Šulc
Supervisor:
Department of Cybernetics
Synopsis:

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.

Requirements:

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

Syllabus of lectures:

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.

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.

Syllabus of tutorials:

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

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.

Study Objective:

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).

Study materials:

1. Duda, Hart, Stork: Pattern Classification, John Willey and Sons, 2nd edition, New York,2001.

2. Schlesinger, Hlavac: Ten Lectures on Statistical and Structural Pattern Recognition, 2002.

Note:
Further information:
http://cw.fel.cvut.cz/wiki/courses/b4b33rpz/start
Time-table for winter semester 2019/2020:
 06:00–08:0008:00–10:0010:00–12:0012:00–14:0014:00–16:0016:00–18:0018:00–20:0020:00–22:0022:00–24:00 roomKN:E-301Matas J.Drbohlav O.11:00–12:30(lecture parallel1)Karlovo nám.Šrámkova posluchárna K9roomKN:E-132Šerých J.12:45–14:15(lecture parallel1parallel nr.101)Karlovo nám.Laboratoř PCroomKN:E-132Špetlík R.14:30–16:00(lecture parallel1parallel nr.103)Karlovo nám.Laboratoř PC roomKN:E-230Šulc M.12:45–14:15(lecture parallel1parallel nr.102)Karlovo nám.Laboratoř PCroomKN:E-230Naiser F.14:30–16:00(lecture parallel1parallel nr.104)Karlovo nám.Laboratoř PC
Time-table for summer semester 2019/2020:
Time-table is not available yet
The course is a part of the following study plans:
Data valid to 2020-08-10
For updated information see http://bilakniha.cvut.cz/en/predmet4683806.html