The lectures are held in the building of UTIA CAS, Pod vodárenskou věží 4, Prague 8. Transportation: metro C, stop Ládví. Meet students in the lobby 10 minutes before the start of the class.

24.09. - 09:30 lecture IN-PERSON

01.10. - 09:30 lecture IN-PERSON

08.10. - 09:30 lecture IN-PERSON

15.10. - 09:30 labs ON-LINE

22.10. - 09:30 lecture IN-PERSON

29.10. - CANCELED

05.11. - 09:30 lecture IN-PERSON

12.11. - 09:30 lecture IN-PERSON

19.11. - 09:30 labs ON-LINE

26.11. - 09:30 lecture IN-PERSON

03.12. - 09:30 labs ON-LINE

10.12. - 09:30 labs ON-LINE

17.12. - 09:30 labs ON-LINE

07.01. - 09:30 labs for undefended projects ON-LINE

The exercises take place online on Zoom.

Lecture time: Wednesday, 09:30 - 12:00.

All students, who signed up for the course in KOS, will receive an email with time confirmation and other instructions.

• Introduction to artificial intelligence, basic concepts

• Decision-making and recognition as an optimization problem

• Theory of feature recognition, "handcrafted" and "learned" features

• Intuitive classifiers with learning - NN classifier, k-NN classifier, linear classifier

• Support Vector Machines (SVM) classifiers, simple neural networks

• Bayesian approach to recognition

• Classification without learning - cluster analysis in the posterior of features, iterative and hierarchical methods

• Dimensionality reduction of the feature space, PCA, discriminant analysis

The files contain most of the slides used. This is not the complete lecture content; the knowledge recorded in the files is insufficient to pass the exam!

[A] Introduction to Object Recognition. It contains many useful references for deeper study.

[1] Duda R.O. et al., Pattern Classification, (2nd ed.), John Wiley, New York, 2001

• An excellent textbook that covers most of the lectures in depth. Free download on several servers. Some parts of the book are obsolete.

[2] Feature selection [CZ].

• Supplementary text.

Will be published during the semester.

Lab 1 - SU101 - 15. 10. 2025 - 09:30 a.m.

Introduction to Colab, GitHub, Python, Machine learning, Linear Regression, K-NN

GitHub link

Lab 2 - SU102 - 19. 11. 2025 - 09:30 a.m.

SVM, Naive Bayes, K-Means Clustering

Lab 3 - SU103 - 03. 12. 2025 - 09:30 a.m.

SVM, Naive Bayes, K-Means Clustering

Lab 4 - SU104 - 10. 12. 2025 - 09:30 a.m.

SVD, PCA, LDA

Lab 5 - SU105 - 17. 12. 2025 - 09:30 a.m.

RANSAC, AdaBoost

Lab 0 - SU100 - 07. 01. 2026 - 09:30 a.m.

Final project prezentation

We are going to take a step into the world of Machine Learning using Python. In order to do that, you should know the basics of Python. Please review the following section before attending the labs. Necessary information for completing all labs will be provided here. If you have any questions, please contact novozamsky@utia.cas.cz

The exercises will be in Czech, but all materials and texts will be in English.

Zoom account

GitHub account

Gmail account for Colab is recommended

Browser

You should know:

• Basic Python Course

At the beginning of every lab session, join Zoom Meeting, where you receive a link for a GitHub classroom assignment. It contains all codes for online/offline studying.

1. Zoom link: https://cesnet.zoom.us/j/96331545797
2. Get a link from your teacher, e.g. https://classroom.github.com/a/YspbK****
3. Sign in to GitHub
   • What is GitHub?
   • New to GitHub? Create an account.
   • Start using GitHub.
4. If necessary, click on the "Authorize GitHub Classroom" button
5. Find your name in the list (only for the first assignment)
6. Click on "Accept the assignment"
7. Go to colab.research.google.com
8. Click on "Authorize Google Colab"
9. Click on "GitHub" (or File > Open Notebook > GitHub)
10. Type your GitHub user name (check "Include private repos")
11. Find the desired repository and the notebook file
12. Loading (In case of "Error", press "Retry")
13. If necessary, click on the "Authorize with GitHub" button
14. Edit the notebook
15. Save in GitHub (File > Save a copy in GitHub)