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Lectures

Lectures will be delivered by Matej Hoffmann or 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 Rock-Paper-Scissors implementation. (TS) rock-paper-scissors.pdf
26.02.2018 2. O Cybernetics and AI - motivation. Goal-directed machine. Is every problem solvable? kui-01-intro.pdf
05.03.2018 3. E Solving problems by search. Trees and graphs. How to avoid getting stuck in dead ends. 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. 03_search.pdf
19.03.2018 5. E Two player-games. Adversarial search - Search when playing against a (rational) opponent. (TS) 04_adversarial.pdf
26.03.2018 6. O Games with random elements, multi-player games. Expectimax. 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. 06_mdp.pdf
16.04.2018 9. E Decision-making under uncertainty II. Policy iteration. 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) 08_rl.pdf
30.04.2018 11. E Mid-term exam from topics covered up to now - inspiration: quizzes. Lecture: Bayesian classification and decisions. 10_bayes.pdf
07.05.2018 12. O Bayesian classification and decisions. 10_bayes.pdf (TS)
14.05.2018 13. E Classification - Perceptron, k-nn and relationship to Bayesian classifier. 11_recog.pdf
21.05.2018 14. O Learning probabilistic models from data - Maximum Likelihood (ML) estimation 12_mle.pdf. Additional resources: matas_pr_03_parameter_estimation_2016_10_17.pdf
courses/be5b33kui/lectures/start.txt · Last modified: 2018/05/21 17:23 by svobodat