====== 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. Note, however, that 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.

Current tentative schedule is right below. For the time being, the schedule from last year with links to slides is kept further down.


^ date ^ week nr. ^ content ^
| 18.02.2019 | 1  | Rules of the game (grading, assignments, etc.). Cybernetics and AI - motivation. Goal-directed machine. Is every problem solvable?  {{ :courses:be5b33kui:lectures:01_intro_mh_2019_v1.0_ekui_compressed.pdf |}}  |
| 25.02.2019 | 2  | Solving problems by search. Trees and graphs. Completeness, Optimality, Complexity. DFS, BFS. {{ :courses:b3b33kui:prednasky:02_search.pdf |}} |
| 04.03.2019 | 3  | Solving problems by search. How to avoid looping forever and how to go faster to the goal. Informed search. Heuristics. A*. {{ :courses:b3b33kui:prednasky:03_search.pdf |}}|
| 11.03.2019 | 4  | Two player-games. Adversarial search - Search when playing against a (rational) opponent. {{ :courses:b3b33kui:prednasky:04_adversarial.pdf |}} (TS)|
| 18.03.2019 | 5  | Games with random elements, multi-player games. Expectimax. Utilities. {{ :courses:be5b33kui:lectures:05_expectimax_withNotes.pdf |}}  |
| 25.03.2019 | 6  | Decision-making under uncertainty I. Route to goal when action outcome is probabilistic. Value iteration. {{ :courses:be5b33kui:lectures:06_mdp_withnotes.pdf |}} |
| 01.04.2019 | 7  | Decision-making under uncertainty II. Policy iteration. {{ :courses:be5b33kui:lectures:07_mdp_withnotes.pdf |}} |
| 08.04.2019 | 8  | Reinforcement learning. What if nothing is known about the probability of action outcomes and we have to learn from final success or failure? (TS) {{ :courses:b3b33kui:prednasky:08_rl.pdf |}}  |
| 15.04.2019 | 9  | Reinforcement learning II. {{ :courses:be5b33kui:lectures:09_rl_withnotes.pdf |}} |
| 22.04.2019 | 10  | Holiday - Easter Monday|
| 29.04.2019 | 11 | **Mid-term exam** from topics covered up to now - inspiration: quizzes.|
| 06.05.2019 | 12 | Bayesian classification and decisions. {{ :courses:be5b33kui:lectures:10_bayes_withnotes.pdf |}} |
| 13.05.2019 | 13 | Bayesian classification, ROC characteristics, k-nn and relationship to Bayesian classifier.  {{ :courses:be5b33kui:lectures:11_recog_a_withnotes.pdf |}} {{ :courses:be5b33kui:lectures:11_recog_b_withnotes.pdf |}}   |
| 20.05.2019 | 14 | Classification in feature space. Discriminant functions. Linear separability. Nearest neighbor classification. Perceptron algorithm. {{ :courses:be5b33kui:lectures:11_recog_b_withnotes.pdf |}}. Maximum likelihood estimation - very brief intro (15 min.), not for exam. |