Lectures are delivered by Matej Hoffmann or Tomas Svoboda (TS).

The lectures start in the Distance teaching mode and continue so until we receive other instructions, taking into account FEE Covid19 info.

On-line teaching (in real time) and discussion will be using BigBlueButton.org. You will be invited to every session by email. You can also find the link to the course on BRUTE, on Course ⇒ Conference rooms. Within the BRUTE you will also see recordings in several formats:

  • Internal viewer - full recording includes slides or shared screen, video streams, and chat. Please note the playing may not work in Safari properly, use other browsers. Video stream and slide screen can be interchanged.
  • MP4 - video rendered from slide screen or shared screen
  • PDF - pdf with all drawings made during the on-line session.

You can also watch the MP4 recordings at the BE5B33KUI YouTube channel

Unless otherwise specified, on-line teaching (lectures/labs) will take place at the times specified in the timetable (same as for standard physical mode). However, often, you will be asked to watch an online lecture from Intro to AI - course from UC Berkeley prior to the online lecture. The lecture will then serve to recapitulate and discuss the topics. 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. If you spot mistakes in the slides, please report them. Important corrections may be rewarded with bonus points. Active participation in lectures is welcomed and encouraged.

Books, on-line resources, specialization courses will be referenced throughout the lectures - typically from the last lecture slide.

date week nr. content
15.02.2021 on-line 1 Rules of the game (grading, assignments, etc.). Cybernetics and AI - motivation. Course overview. Is every problem solvable? N-puzzle. 01_intro.pdf
22.02.2021 on-line 2 Solving problems by search. Trees and graphs. Completeness, Optimality, Complexity. DFS, BFS. 02_search.pdf, 02_search_handout_notes.pdf
01.03.2021 online 3 Solving problems by search. How to avoid looping forever and how to go faster to the goal. Informed search. Heuristics. A*. 03_search.pdf 03_search_live_withnotes.pdf
08.03.2021 online 4 Two player-games. Adversarial search - Search when playing against a (rational) opponent. Watch online video from 6 - Adversarial search, AI@Berkeley prior to lecture. Our slides: 04_adversarial.pdf 04_adversarial_live_withnotes.pdf .
15.03.2021 online 5 Games with random elements, multi-player games. Expectimax. Utilities. Watch online video from 7 - Expectimax and Utilities, AI@Berkeley prior to lecture. 05_expectimax.pdf 05_expectimax_live_withnotes.pdf
22.03.2021 online 6 Decision-making under uncertainty I. Route to goal when action outcome is probabilistic. Value iteration. Watch online video from 8 - Markov decision processes I, AI@Berkeley prior to lecture. 06_mdp.pdf 06_mdp_live_withnotes.pdf
29.03.2021 online 7 Decision-making under uncertainty II. Policy iteration. Watch online video from 9 - Markov decision processes II, AI@Berkeley prior to lecture. 07_mdp.pdf 07_mdp_live_withnotes.pdf
5.04.2021 Holiday - Easter Monday
12.04.2021 online 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? Watch online video from 10 - Reiforcement learning I, AI@Berkeley prior to lecture. 08_rl.pdf 08_rl_live_withnotes.pdf
19.04.2021 online 9 Reinforcement learning II. Exploration vs. exploitation. Watch online video from 11 - Reiforcement learning II, AI@Berkeley prior to lecture. 09_rl.pdf 09_rl_live_withnotes.pdf
26.04.2021 online 10 Mid-term exam from topics covered up to now. Inspiration: quizzes.
3.05.2021 online 11 Basic concepts of probability (see also: online video from 12 - Probability, AI@Berkeley) . Bayesian classification and decisions. 10_bayes.pdf 10_bayes_live_withnotes.pdf
10.05.2021 online 12 Naive Bayesian classification, Laplace smoothing, Precision, Recall and ROC curve. Watch online video from 21 - Machine Learning: Naive Bayes, AI@Berkeley prior to lecture. (additional online materials: ROC curve, video by Andrew Ng) 11_recog_a.pdf 11_recog_a_live_withnotes.pdf
17.05.2021 online 13 Linear classifiers, perceptron. Watch online video from 22 - Machine Learning: Perceptrons, AI@Berkeley prior to lecture. For Nearest neighbor (k-nn) classification, watch 23 - Machine Learning: Kernels and Clustering (only up to min. 16). 11_recog_b.pdf 11_recog_b_live_withnotes.pdf
courses/be5b33kui/lectures/start.txt · Last modified: 2021/05/17 11:49 by hoffmmat