====== 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.

(w-week number; Odd/Even teaching week)
^ date ^ w ^ O/E ^ content ^
| 19.02.2018 |  1. | E | Rules of the game (grading, assignments, etc.). Introduction to (Objective) Python. We will design [[https://en.wikipedia.org/wiki/Rock–paper–scissors|Rock-Paper-Scissors]] implementation. (TS) {{ :courses:be5b33kui:lectures:rock-paper-scissors.pdf |}} |
| 26.02.2018 |  2. | O | Cybernetics and AI - motivation. Goal-directed machine. Is every problem solvable? {{ :courses:b3b33kui:prednasky:kui-01-intro.pdf |}} |
| 05.03.2018 |  3. | E | Solving problems by search. Trees and graphs. How to avoid getting stuck in dead ends. {{ :courses:be5b33kui:lectures:02_search.pdf |}}(TS) |
| 12.03.2018 |  4. | O | Solving problems by search. How to avoid looping forever and how to go faster to the goal. Informed search. Heuristics. {{ :courses:be5b33kui:lectures:03_search.pdf |}}|
| 19.03.2018 |  5. | E | Two player-games. Adversarial search - Search when playing against a (rational) opponent. (TS) {{ :courses:b3b33kui:prednasky:04_adversarial.pdf |}}|
| 26.03.2018 |  6. | O | Games with random elements, multi-player games. Expectimax. {{ :courses:b3b33kui:prednasky:05_expectimax.pdf |}}  |
| 02.04.2018 |  7. | E ^ Easter - holiday, no teaching |
| 09.04.2018 |  8. | O | Decision-making under uncertainty I. Route to goal when action outcome is probabilistic. Value iteration. {{ :courses:b3b33kui:prednasky:06_mdp.pdf |}}|
| 16.04.2018 |  9. | E | Decision-making under uncertainty II. Policy iteration.  {{ :courses:be5b33kui:lectures:07_mdp_mh.pdf |}}|
| 23.04.2018 |  10. | O | 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 |}} |
| 30.04.2018 |  11. | E | **Mid-term exam** from topics covered up to now - inspiration: quizzes. Lecture: Bayesian classification and decisions. {{ :courses:b3b33kui:prednasky:10_bayes.pdf |}}|
| 07.05.2018 |  12. | O | Bayesian classification and decisions. {{ :courses:b3b33kui:prednasky:10_bayes.pdf |}} (TS) |
| 14.05.2018 |  13. | E | Classification - Perceptron, k-nn and relationship to Bayesian classifier. {{ :courses:b3b33kui:prednasky:11_recog.pdf |}} |
| 21.05.2018 |  14. | O | Learning probabilistic models from data - Maximum Likelihood (ML) estimation {{ :courses:b3b33kui:prednasky:12_mle.pdf |}}. Additional resources: {{ :courses:be5b33kui:lectures:matas_pr_03_parameter_estimation_2016_10_17.pdf |}}   |