CSE 571 Artificial Intelligence Fall 2013 Schedule

  1. 8/26: Intro
  2. 8/28: Intro (Audio of lecture 2)

  3. 9/4: MDP(Audio of lecture 3 (9/4))

  4. 9/9: MDP (Audio of lecture 4 (9/9))
  5. 9/11: MDP (Slide video of lecture 5 (9/11))

  6. 9/16 Infinitie Horizon MDP, Value Iteration, its convergence (Audio of Lecture 6) ((Partial Slide video of lecture 6 (9/16))
  7. 9/18: Policy Iteration; Stochastic Shortest Path MDP (Slide video of lecture 7 (9/18))

  8. 9/23: More on SSP; compiling FH and IHDR to SSP; RL start (Slide video of lecture 8 (9/23))
  9. 9/25: RL Dimensions: Model-based vs. Model-free; active vs. passive;-- --simulator vs real world. Model-based and Model-free-- learning for passive RL. Model-based for active RL. The importance of exploration (Slide video of lecture 9 (9/25))

  10. 9/30 (MDP Homework Due) Exploration policies; passive model-free learning and temporal-difference learning. Relating temporal difference learning and montecarlo learning. The concept of k-step returns and TD(\lamda) learning. Active model-free learning, and the need to keep track of Q-values. (Slide video of lecture 10 (9/30))
  11. 10/2 RL end; start of approximation approaches for MDPs (Slide video of lecture 11 (10/2))

  12. 10/7: LRTDP and UCP discussion. (Slide Video)
  13. 10/9 (no-class; see videos I pointed you to).

  14. 10/16 POMDP Model (Slide Video)

  15. 10/21 POMDP Model contd. (Slide Video)
  16. 10/23 End of POMDP Model (Slide Video)

  17. 10/28 Midterm (Tentative)(includes MDP,RL,POMDP)(Rao on travel)
  18. 10/30 Overview of Factored Approaches for MDPs and Reinforcement Learning (Slide Video)

  19. 11/4 Factored Reinforcement Learning--and discussion on gradient-descent approaches for regression; Policy Gradient (Slide Video)
  20. 11/6 (Discussed Bayes Network inference--under the assumption-- --that you watched L19 and L20)

  21. 11/13 Discussion of MLE approaches for learning on Bayes Networks Learning (Slide Video)

  22. 11/18 Bayesian Learning with continuous hypothesis spaces (Slide Video)
  23. 11/20: Plate Models & Markov Networks--a quick introduction (Slide Video)
  24. 11/22 (Extra Class): Markov Networks continued; conditional independences, inference, learning (and some discussion on connections to deep learning) (Slide Video)

  25. 11/25 Start of statistical models of language. (Slide Video)

  26. 11/27: Delving deep into topic models, understanding the nature of the topics, seeing the connection with dimensionality reduction, supervised vs. unsupervised learning etc. Going from unigram to single topic to multi-topic model(Slide Video)

  27. 12/2: Discussion on LDA model and its applications (Slide Video)
  28. 12/4: Inference on LDA model (Slide Video)

Final exam scheduled on 12/11 from 12:10pm--2:00pm (see university finals schedule)