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Lecture 11 slides updated on May 18.

The lectures are given in English to all students. Lecture slides up to the latest lecture: smu-slides.pdf.

The book links below pointing to Springer Link let you download the PDFs if accessing from the CVUT domain. Don't get confused by the indexing in the Universal AI book: it is different from ours because Hutter assumes the agent-env interaction to start with an agent's action whereas we start with a percept from the environment (for us, this is more natural in the reinforcement and concept learning scenarios).

Lec # Lecture date Slides available Online Meeting (Planned) Contents Additional Reading
1 17.2 17.2 General framework, passive reinforcement learning, DUE and ADP agents AIMA book Chapter 21, RL Book
2 24.2 24.2 TD agent, active R/L, Q-learning as above
2.3 (school closed)
3 9.3 9.3 SARSA agent, state representation, policy search, Bayesian approach as above (except Bayesian)
4 23.3 30.3 16:15 Finish Bayesian approach. Universal sequence prediction, AIXI agent; Intro to concept learning. Universal AI Book Chapter 1 (optionally other chapters for deeper understanding); supplementary web
5 30.3 6.4 16:15 Online concept learning, mistake-bound model numerous sources here
6 6.4 13.4 16:15 Batch-learning, PAC-learning model as above
7 17.4 20.4. 16:15 Finish PAC-learning. Learning relational concepts ILP Book, Logical Learning Book
8 26.4 27.4 16:15Reduction, Learning with Background Knowledge as above
9 4.5 4.5 16:15 Functions, Inductive Logic Programming, Structured Output as above + AIMA book Ch. 9.4.2 and 19.5; ILP wiki
10 11.5 11.5 16:15 Bayesian networks - Conditional independence, Inference AIMA book Ch. 14
11 18.5 18.5 16:15 Bayesian networks - MAP inference, parameter learning AIMA book Ch. 20.2.4 (MAP Inference not in AIMA)
Learning with queries

Consultations: please ask your questions in the forum and/or at the online meeting.

courses/smu/lectures.txt · Last modified: 2020/05/18 15:48 by zelezny