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