Pattern Recognition and Machine Learning

Vztahy:

During a review of study plans, the course B4B33RPZ can be substituted for the course BE5B33RPZ.

It is not possible to register for the course BE5B33RPZ if the student is concurrently registered for or has already completed the course B4B33RPZ (mutually exclusive courses).

It is not possible to register for the course BE5B33RPZ if the student is concurrently registered for or has already completed the course A4B33RPZ (mutually exclusive courses).

In order to register for the course BE5B33RPZ, the student must have registered for the required number of courses in the group BEZBM no later than in the same semester.

It is not possible to register for the course BE5B33RPZ if the student is concurrently registered for or has previously completed the course B4B33RPZ (mutually exclusive courses).

Garant předmětu:

Jiří Matas

Lecturer:

Ondřej Drbohlav, Jiří Matas

Tutor:

Ondřej Drbohlav, Jiří Matas, Lukáš Neumann, Oleksandr Shekhovtsov, Jan Šochman, Tomáš Vojíř

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.

This course is also part of the inter-university programme prg.ai Minor. It pools the best of AI education in Prague to provide students with a deeper and broader insight into the field of artificial intelligence. More information is available at https://prg.ai/minor.

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.

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.

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

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.

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, 2001.

2.Bishop: Pattern Recognition and Machine Learning, 2006.

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

Note:

Further information:

https://cw.fel.cvut.cz/wiki/courses/BE5B33RPZ

Time-table for winter semester 2024/2025:

	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
Mon	roomKN:E-301 Matas J. Drbohlav O. 16:15–17:45 (lecture parallel1) Karlovo nám. Šrámkova posluchárna K9room 18:00–19:30 (lecture parallel1 parallel nr.103)
Tue
Wed
Thu	roomKN:E-132 12:45–14:15 (lecture parallel1 parallel nr.101) Karlovo nám. Laboratoř PC roomKN:E-126 12:45–14:15 (lecture parallel1 parallel nr.102) Karlovo nám. Trnkova posluchárna K5
Fri

Time-table for summer semester 2024/2025:

Time-table is not available yet

The course is a part of the following study plans:

• Electrical Engineering and Computer Science (EECS) (compulsory elective course)
• Open Informatics - Computer Science 2016 (compulsory course in the program)
• Open Informatics - Internet of Things 2016 (compulsory course in the program)
• Open Informatics - Software 2016 (compulsory course in the program)
• Open Informatics - Computer Games and Graphics 2016 (compulsory course in the program)
• Open Informatics (compulsory course in the program)
• Open Informatics (compulsory elective course)
• Open Informatics - Artificial Intelligence and Computer Science 2018 (compulsory elective course)
• Open Informatics - Internet of Things 2018 (compulsory elective course)
• Open Informatics - Software 2018 (compulsory elective course)
• Open Informatics - Computer Games and Graphics 2018 (compulsory elective course)
• Electrical Engineering and Computer Science (EECS) (compulsory elective course)
• prg.ai/minor-tech (elective course)