Lectures

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  • 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.
  • 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.

Lecture plan

Date Week Content Alternative lecture video from AI@Berkeley
16.02.2026 BBB rec. with tech. issues, BBB rec. from last year 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
23.02.2026 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
02.03.2026 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
09.03.2026 4 Probability and statistics - the required minimum. 045_probability.pdf 045_probability_handout_notes.pdf Probability
16.03.2026 5 Games with random elements, multi-player games. Expectimax. Utilities. 05_expectimax.pdf 05_expectimax_handout_notes.pdf Uncertainty and utilities
23.03.2026 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
30.03.2026 7 Decision-making under uncertainty II. Policy iteration. 07_mdp.pdf 07_mdp_handout_notes.pdf Markov Decision Processes II
06.04.2026 8 Holiday
13.04.2025 9 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
20.04.2026 10 Reinforcement learning II. Exploration vs. exploitation. 09_rl.pdf 09_rl_handout_notes.pdf Reinforcement learning II
27.04.2026 11 Mid-term exam from topics covered up to now. Inspiration: quizzes, lab exercises.
04.05.2026 12 Bayesian classification and decisions. 10_bayes.pdf 10_bayes_handout_notes.pdf
11.05.2026 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
18.05.2026 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

Newer recordings of CS188 lectures from Berkeley

courses/be5b33kui/lectures/start.txt · Last modified: 2026/02/16 14:40 by xposik