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Table of Contents

Lectures

  • Tuesdays, 12:45-14:15. BBB room or KN:E-301.
  • Lecturers: Petr Pošík (PP), Radek Mařík (RM)

List of competencies you should have after individual lectures. (Will be updated continuously.)

The table below shows the links to upcoming lecture rooms/passed lecture recordings. You can also find the links to rooms and recordings in BRUTE, or in the FELSight portal.

Schedule

The order of lectures is subject to change.
The PDFs are from previous years. They will be updated during the semester.

Date W# Who Contents Materials
16.02. 1 PP AI, PR, learning and robotics. Decision tasks. Empirical learning. Slides. Handouts.
23.02. 2 PP Linear methods for classification and regression. Slides. Handouts.
02.03. 3 PP Non-linear models. Feature space straightening. Overfitting. Slides. Handouts.
09.03. 4 PP Nearest neighbors. Kernel functions, SVM. Decision trees. Slides. Handouts.
16.03. 5 PP Bagging. Adaboost. Random forests. Slides. Handouts.
23.03. 6 PP Neural networks. Basic models and methods, error backpropagation. Slides. Handouts.
30.03. 7 PP Deep learning. Convolutional and recurrent NNs. Slides. Handouts.
06.04. 8 PP Probabilistic graphical models. Bayesian networks. Slides. Handouts.
13.04. 9 PP Hidden Markov models. Slides. Handouts.
20.04. 10 PP Expectation-Maximization algorithm. Slides. Handouts.
27.04. 11 RM Planning. Planning problem representations. Planning methods. Handouts
04.05. 12 RM Constraint satisfaction problems. Handouts
11.05. 13 RM Scheduling. Local search. Handouts
18.05. 14 PP Reserve. Summarizing run through the topics.
courses/ui/lectures.txt · Last modified: 2021/03/23 16:07 by xposik