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Lectures

Please be aware that Mondays are largely affected by Public holidays. Some Mondays are thus substituted by other days. Lectures will be delivered by Tomas Svoboda unless stated otherwise.

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.

date week topic
20.02.2017 1. Intro into course and cybernetics and AI. We learn about the course grading and its rules as well as about what connects the cybernetics and artificial intelligence. I show some real applications of the methods we learn during the courses in an attempt to make the first lecture not too much dry and boring. kui-01-intro.pdf, chapter01.pdf
27.02.2017 2. Solving problems by search. We learn how to formalize problems and how to design algorithms for finding a solution. chapter03.pdf, kui-02-search.pdf (not completed, continue on March 13)
06.03.2017 3. Embodied artificial intelligence, a lecture by Matej Hoffmann - a slight diversion into an interesting perspective on AI and robotics.
13.03.2017 4. Uniformed search, properties, comparisons. kui-03-search.pdf
20.03.2017 5. Informed search; kui-04-informed-search.pdf We find a way how to make search more effective by inserting some knowledge about the problem. Adversarial search. How to find an optimal move/solution when someone or something is playing against us.
27.03.2017 6. Adversarial search. How to find an optimal move/solution when someone or something is playing against us. chapter06.pdf, kui-05-adversarial.pdf. Sequential decisions. What if an action (robot command, selling order) has an uncertain but probable outcome? How to compute the utilities?
03.04.2017 7. Sequential decisions. kui-06-mdp.pdf Value iteration, first compute state utilities than extract the best policy.
10.04.2017 8. Sequential decisions. Estimate the right policy directly - policy iteration method. kui-07-mdp.pdf Intro into the reinforcement learning.
17.04.2017 9. Public holiday - Easter Monday
24.04.2017 10. Reinforcement learning. What if nothing is known about the probability of action outcomes and we have to learn from final success or failure? kui-08-rl.pdf
01.05.2017 11. Public holiday
02.05.2017 11. Bayesian classification and decisions - Matej Hoffmann. How to decide optimally if we know all the (conditional) probabilities. kui-11-bayes.pdf
08.05.2017 12. Public holiday
11.05.2017 12. Classification - Matej Hoffmann. Perceptron, k-nn and relationship to Bayesian classifier - kui-12-classification.pdf
15.05.2017 13. Learning probabilistic models - Matej Hoffmann. Slides from Jiri Matas: matas_pr_03_parameter_estimation_2016_10_17.pdf - up to slide 15.
22.05.2017 14. Solving problems on paper/blackboard - fully interactive lecture; preparing for the exam a bit
courses/be5b33kui/lectures/start.txt · Last modified: 2018/02/18 15:41 by svobodat