Pattern Recognition and Machine Learning

The course is not on the list Without time-table
Code Completion Credits Range Language
AE4B33RPZ Z,ZK 6 2+2c
The course cannot be taken simultaneously with:
Pattern Recognition and Machine Learning (A4B33RPZ)
The course is a substitute for:
Pattern Recognition and Machine Learning (A4B33RPZ)
Department of Cybernetics

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.


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).


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, 2001.

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

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

Further information:
No time-table has been prepared for this course
The course is a part of the following study plans:
Data valid to 2019-03-24
For updated information see http://bilakniha.cvut.cz/en/predmet12820204.html