Lectures and Reading

  1. Jan. 16, Introduction (R&N Ch. 2), Intro (PowerPoint slides), Vehicles handout
  2. Jan. 19, Review of basic probability theory--conditional independence, Bayes Rule. (R&N Ch. 14)
  3. Jan. 23, Review of basic probability theory--conditional independence, Bayes Rule. (R&N Ch. 14) (cont.)
  4. Jan. 25, Bayesian Classification (handout: page 1, page 2, page 3)
    [extra copies of handout and homework in the bin near 725 Soda Hall]
  5. Jan. 30, Classification (handout in class, extras in 725 Soda)
  6. Feb. 1, Learning: Single layer perceptrons (B&N Ch. 19)
  7. Feb. 6, Learning: Single layer perceptrons (cont.)
  8. Feb. 8, Learning: Multi-layer perceptrons
  9. Feb. 13, Learning: Multi-layer perceptrons (cont.), k-NN classifier
  10. Feb. 15, Decision Trees (R&N Ch. 18.1-18.4), Belief Networks (R&N Ch. 15)
  11. Feb. 20, Belief Networks (cont.)
  12. Feb. 22, Belief Networks (cont.), Hidden Markov Models (handouts: HMM Tutorial and Jodran-Bishop paper)
  13. Feb. 27, HMMs (cont.)
  14. March 1, HMMs (cont.)
  15. March 6, HMMs (Viterbi) (R&N Ch. 24.7)
  16. March 8, Speech
  17. March 13, MIDTERM
  18. March 15, Vision (R&N Ch. 24.1-24.6)
  19. March 20, Vision (cont.)
  20. March 22, Vision (cont.)
  21. April 3, Making Simple Decisions (R&N Ch. 16.1,16.3)
  22. April 5, Making Complex Decisions (R&N Ch. 17.1-3)
  23. April 10, Game Theory (R&N Ch. 5), HW#4 DUE!
  24. April 12, Reinforcement Learning (R&N Ch. 20 + handout)
  25. April 17, Reinforcement Learning (cont.)
  26. April 19, Reinforcement Learning (cont.)
  27. April 24, Control Theory (handout)
  28. April 26, Search and Planning (R&N Ch. 3.1-3.5, Ch. 11.1,11.2,11.4)
  29. May 1, Natural Language Processing (R&N Ch. 22.1-22.4,22.8), HW#6 DUE!
  30. May 3, NLP (cont.)
  31. May 8, Review
  32. May 15, FINAL EXAM: 8-11am, 10 Evens Hall