Lectures

  • Lectures are delivered by Petr Pošík or Tomas Svoboda (TS).
  • We strongly suggest attending the lectures personally. Active participation in lectures is welcomed and encouraged.
  • The lectures will have a hybrid form, they will be streamed and recorded. BigBlueButton integrated in BRUTE will be used to deliver the lectures.
  • The link to the course room will be available in BRUTE, at Course ⇒ Conference rooms.
  • The lecture recordings will also be available in BRUTE in several formats:
    • Internal viewer - full recording, including slides or shared screen, video stream (blackboard), and chat. Please note the BBB player may not work in Safari properly, use another browser. Video stream (showing blackboard in the classroom or shared screen) and the slide screen can be interchanged.
    • MP4 - video containing only slides/shared screen. No blackboard recording.
    • PDF - pdf with all drawings made during the on-line session into the slides.
  • You can also watch the MP4 recordings from the distance teaching during COVID pandemic at the BE5B33KUI YouTube channel. However, beware that the content of the course might have changed slightly since then.
  • PDF of the slides will be posted on this page. Preliminary version (e.g., last year's) before the lecture. Latest version, including teacher's notes shortly after the lecture.
  • Please report any mistakes you spot in the lectures. Important corrections may be rewarded with bonus points.

Books, on-line resources, specialization courses will be referenced throughout the lectures - their list is typically at the last slide.

Instead of colorful paper voting cards, you can use the following HTML+Javascript app. Open it on your mobile phone: KUI Voting Cards

Lecture plan

Date Week Content Alternative lecture video from AI@Berkeley
17.02.2025 BBB rec. 1 Rules of the game (grading, assignments, etc.). Cybernetics and AI - very short intro. 01_intro.pdf Solving problems by search. Completeness, Optimality, Complexity. DFS, BFS. 025_search.pdf 025_search_handout_notes.pdf (Updated 2025-02-17) Uninformed search
24.02.2025 BBB rec. 2 Solving problems by search. How to avoid looping forever and how to go faster to the goal. Informed search. Heuristics. A*. 025_search.pdf, 025_search_live_withnotes.pdf, 025_search_handout_notes.pdf Informed search
03.03.2025 BBB rec. 3 Two player-games. Adversarial search - Search when playing against a (rational) opponent. 04_adversarial.pdf 04_adversarial_live_withnotes.pdf 04_adversarial_handout_notes.pdf Adversarial search
10.03.2025 BBB rec 4 Probability and statistics - the required minimum. 045_probability.pdf 045_probability_handout_notes.pdf Probability
17.03.2025 BBB rec. 5 Games with random elements, multi-player games. Expectimax. Utilities. 05_expectimax.pdf 05_expectimax_handout_notes.pdf Uncertainty and utilities
24.03.2025 BBB rec. 6 Decision-making under uncertainty I. Route to goal when action outcome is probabilistic. Value iteration. 06_mdp.pdf 06_mdp_handout_notes.pdf Markov Decision Processes
31.03.2025 BBB rec. 7 Decision-making under uncertainty II. Policy iteration. 07_mdp.pdf 07_mdp_handout_notes.pdf Markov Decision Processes II
07.04.2025 BBB rec. 8 Reinforcement learning I. What if nothing is known about the probability of action outcomes and we have to learn from final success or failure? 08_rl.pdf 08_rl_handout_notes.pdf Reinforcement learning
14.04.2025 BBB rec. 9 Reinforcement learning II. Exploration vs. exploitation. 09_rl.pdf 09_rl_handout_notes.pdf Reinforcement learning II
21.04.2025 10 Holiday
28.04.2025 11 Mid-term exam from topics covered up to now. Inspiration: quizzes, lab exercises.
05.05.2025 BBB rec. 12 Bayesian classification and decisions. 10_bayes.pdf 10_bayes_handout_notes.pdf
12.05.2025 BBB rec. 13 Learning from data I. Naive Bayes classifier. Nearest neighbors. Evaluating classifier performance. 11_recog_a.pdf 11_recog_a_handout_notes.pdf Naive Bayes, ROC curve, video by Andrew Ng, Kernels and Clustering - the nearest neighbors part
19.05.2025 BBB rec. 14 Learning from data II. Linear classifiers. 11_recog_b.pdf 11_recog_b_handout_notes.pdf Perceptrons and Logistic Regression - the logistic regression part
courses/be5b33kui/lectures/start.txt · Last modified: 2025/05/19 14:14 by xposik