====== Lectures ======

Lectures will be delivered by [[https://sites.google.com/site/matejhof/|Matej Hoffmann]] or [[http://cmp.felk.cvut.cz/~svoboda|Tomas Svoboda (TS)]].

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. Note, however, that the lectures will contain a significant portion of blackboard writing as well as some live programming pieces. Reading/watching slides only is not enough. Active participation in lectures is welcomed and encouraged.

[[courses:be5b33kui:literature|Books, on-line resources, specialization courses]] will be referenced throughout the lectures - typically from the last lecture slide. 

The schedule will be frequently updated, in particular taking into account [[https://www.fel.cvut.cz/en/covid/|FEE Covid19 info]]. The lectures switch to the [[https://cw.fel.cvut.cz/wiki/courses/be5b33kui/distance_teaching|Distance teaching mode]] from March 23, 2020.

^ date ^ week nr. ^ content ^
| 17.02.2020 | 1  | Rules of the game (grading, assignments, etc.). Cybernetics and AI - motivation. Course overview. Is every problem solvable? N-puzzle.  {{ {{ {{ :courses:b3b33kui:prednasky:01_intro_mh_2020_v5_novideos.pdf |}} |
| 24.02.2020 | 2  | Solving problems by search. Trees and graphs. Completeness, Optimality, Complexity. DFS, BFS.  {{ :courses:be5b33kui:lectures:02_search_withnotes.pdf |}}  |
| 02.03.2020 | 3  | Solving problems by search. How to avoid looping forever and how to go faster to the goal. Informed search. Heuristics. A*. {{ :courses:be5b33kui:lectures:03_search_live_withnotes.pdf |}} |
| 09.03.2020 | 4  | Two player-games. Adversarial search - Search when playing against a (rational) opponent. {{ :courses:be5b33kui:lectures:04_adversarial_withnotes.pdf |}}|
| <del>16.03.2020</del> 23.3.2020 watch on-line  | 5  | Games with random elements, multi-player games. Expectimax. Utilities. Please watch online video: [[https://youtu.be/GevK0-9n24g|AI@Berkeley]] and study lecture slides with notes {{ :courses:b3b33kui:prednasky:05_expectimax_withnotes.pdf |}}.  |
| 30.03.2020 watch online | 6  | Decision-making under uncertainty I. Route to goal when action outcome is probabilistic. Value iteration. Please watch online video: [[https://youtu.be/wKx4MuLfe0M|AI@Berkeley]] and study lecture slides with notes  {{ :courses:be5b33kui:lectures:06_mdp_withnotes.pdf |}} |
| 06.04.2020 watch online | 7  | Decision-making under uncertainty II. Policy iteration. Please watch online video: [[https://youtu.be/2M7mv4-BPCg|AI@Berkeley]] and study lecture slides with notes {{ :courses:be5b33kui:lectures:07_mdp_withnotes.pdf |}} |
| 13.04.2020 | 8  | Holiday - Easter Monday|
| 20.04.2020 watch online | 9  | Reinforcement learning. What if nothing is known about the probability of action outcomes and we have to learn from final success or failure? Please watch online video: [[https://youtu.be/hsz0zq6AXGE|AI@Berkeley]] and study lecture slides with notes {{ :courses:be5b33kui:lectures:08_rl_live_withnotes.pdf |}}  |
| 27.04.2020 watch online | 10 | Reinforcement learning II. Exploration vs. exploitation. Please watch online video: [[https://youtu.be/R0vTZp0ve4s|AI@Berkeley]] and study lecture slides with notes {{ :courses:be5b33kui:lectures:09_rl_withnotes.pdf |}}|
| 04.05.2020 online  | 11 | **Mid-term exam** from topics covered up to now. Inspiration: quizzes. |
| 11.05.2020 online | 12 | Bayesian classification and decisions. {{ :courses:be5b33kui:lectures:10_bayes.pdf |}} {{ :courses:be5b33kui:lectures:10_bayes_live_withnotes.pdf |}} [[https://bbb04.felk.cvut.cz//playback/presentation/2.0/playback.html?meetingId=f9340240c51f550c2eb4d644222fb72590b1d697-1589183401403| BBB recording]] |
| 18.05.2020 watch online | 13 | Naive Bayesian classification, Laplace smoothing, Precision, Recall and ROC curve. Please watch online video [[https://youtu.be/_pe60buCnxE|AI@Berkeley: Naive Bayes and Laplace smoothing]], study [[https://en.wikipedia.org/wiki/Precision_and_recall |Precision, Recall]]  and [[https://www.coursera.org/lecture/python-machine-learning/precision-recall-and-roc-curves-8v6DL| ROC curve]] (or this [[ 
https://youtu.be/W5meQnGACGo |video by Andrew Ng]]) or consult lecture slides with notes {{ :courses:be5b33kui:lectures:11_recog_a_live_withnotes.pdf |}}.  |
| 25.05.2020 watch online  | 14 | Linear classifiers, perceptron (watch [[https://www.youtube.com/watch?v=HEFfs1KCph4|AI@Berkeley: Perceptrons]]). Nearest neighbor (k-nn) classification (watch [[https://www.youtube.com/watch?v=H9DUTH9lCfg|AI@Berkeley: Machine Learning: Kernels and Clustering]]  - only up to min. 16). Consult lecture slides with notes {{ :courses:be5b33kui:lectures:11_recog_b_withnotes.pdf |}}.  |