BE5B33RPZ -- Pattern Recognition and Machine Learning

Summary

This course introduces statistical decision theory and surveys canonical and advanced classifiers such as perceptrons, AdaBoost, support vector machines, and neural nets.

Basic info

Winter semester 2023/2024

Teaching: Jiří Matas (JM) matas@cmp.felk.cvut.cz, Ondřej Drbohlav (OD) drbohlav@cmp.felk.cvut.cz

Where and when: KN:E-301 at Building G, Karlovo namesti, Monday 16:15-17:45

Lectures are streamed live on YouTube at the time of lecture: online stream of the lecture room KN:E-301

Recorded lectures playlist

Last year (2022) lecture playlist

Lecture plan 2023/2024

Week	Date	Lect.	Slides	Topic	Wiki	Additional material
1	25.9.	JM	pdf	Introduction. Basic notions. The Bayesian recognition problem	Machine_learning Naive_Bayes_classifier	some simple problems
2	2.10.	OD	pdf	Non-Bayesian tasks	Minimax
3	9.10.	JM	pdf	Parameter estimation of probabilistic models. Maximum likelihood method	Maximum_likelihood
4	16.10.	JM	pdf	Nearest neighbour method. Non-parametric density estimation.	K-nearest_neighbor_algorithm
5	23.10.	JM	pdf	Logistic regression	Logistic_regression
6	30.10.	JM	pdf	Classifier training. Linear classifier. Perceptron.	Linear_classifier Perceptron
7	6.11.	JM	pdf	SVM classifier	Support_vector_machine	demo
8	13.11.	JM	pdf	Adaboost learning	Adaboost
9	20.11.	JM	no teaching	Dean's day
10	27.11.	JM	pdf	Neural networks. Backpropagation	Artificial_neural_network
11	4.12.	JM	pdf	Cluster analysis, k-means method	K-means_clustering K-means++
12	11.12.	JM	pdf	EM (Expectation Maximization) algorithm.	Expectation_maximization_algorithm	Hoffmann,Bishop, Flach
13	18.12.	JM	pdf	Feature selection and extraction. PCA, LDA.	Principal_component_analysis Linear_discriminant_analysis	Optimalizace (CZ): PCA slides, script 7.2
14	8.1.	JM	pdf	Decision trees.	Decision_tree Decision_tree_learning	Rudin@MIT

Duda R.O., Hart, P.E.,Stork, D.G.: Pattern Classification, John Willey and Sons, 2nd edition, New York, 2001

Schlesinger M.I., Hlaváč V.: Ten Lectures on Statistical and Structural Pattern Recognition, Springer, 2002
Bishop, C.: Pattern Recognition and Machine Learning, Springer, 2011
Goodfellow, I., Bengio, Y. and Courville, A.: Deep Learning, MIT Press, 2016. www

Assessment (zápočet)

Conditions for assessment are in the lab section.

Exam

Only students who receive all credits from the lab work and are granted the assessment (“zápočet”) can be examined.
The labs contribute 50% to your final evaluation, the written part of the exam contributes 40% and the oral part 10%.
For passing the written exam, at least 40% of maximum number of points is needed (16 out of 40 points).
Your grade chances after the written exam test and before the oral exam are illustrated in the image below. Beware: The scheme is approximate only, but illustrates how the oral exam influences the final grade.
The questions used in the test are available here (if one can solve these questions, one will likely do well in the exam).
The oral starts approximately 2 hours after the end of the test, as soon as the tests are graded. Alternatively, the oral might take place the following day, if the number of tests to correct is high.
The oral part is compulsory! Note that if during the oral examination it becomes clear that the student cannot explain how they obtained a result in the written exam, the examiner may re-assess the grading of the written exam.
Example oral exam questions are available here.

Table of Contents