Course Outline

  1. Introduction
  2. Review of basic probability theory--conditional independence, Bayes Rule.
  3. Learning, Bayesian classification
  4. Learning: Single layer perceptrons
  5. Learning: Multilayer perceptrons
  6. Learning: Multilayer perceptrons
  7. Probabilistic Reasoning: Belief Networks 1
  8. Probabilistic Reasoning: Belief Networks 2
  9. Speech Recognition
  10. Hidden Markov Models 1
  11. Hidden Markov Models 2
  12. Vision: Early visual processing
  13. Vision: Extracting 3D information from 2D images
  14. Vision: Object Recognition
  15. Midterm
  16. Optimizing actions 1: Making simple decisions
  17. Optimizing actions 2: Sequential decision problems
  18. Optimizing actions 3: Reinforcement learning
  19. Locomotion; feedback control
  20. Navigation and Map making
  21. Game Theory
  22. Logical reasoning
  23. Planning actions in a logical framework
  24. Planning actions in a logical framework
  25. Natural Language Understanding 1
  26. Natural Language Understanding 2
  27. Retrospective