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CZECH TECHNICAL UNIVERSITY IN PRAGUE
STUDY PLANS
2023/2024

Pattern Recognition and Machine Learning

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Code Completion Credits Range Language
BE5B33RPZ Z,ZK 6 2P+2C English

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 2023/2024:
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 K9
room
Drbohlav O.
18:00–19:30
(lecture parallel1
parallel nr.103)

Tue
Wed
Thu
roomKN:E-132
Neumann L.
12:45–14:15
(lecture parallel1
parallel nr.101)

Karlovo nám.
Laboratoř PC
roomKN:E-126
Shekhovtsov O.
12:45–14:15
(lecture parallel1
parallel nr.102)

Karlovo nám.
Trnkova posluchárna K5
Fri
Time-table for summer semester 2023/2024:
Time-table is not available yet
The course is a part of the following study plans:
Data valid to 2024-04-23
Aktualizace výše uvedených informací naleznete na adrese https://bilakniha.cvut.cz/en/predmet4358506.html