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Spring 2017 Tutorial: Artificial Intelligence and Applications

Week 0: (Jan 30) Tutorial mechanics

  • Reading
    • None
  • ToDo
    • None

Week 1: (Feb 6) What is AI?

  • Reading
    • Chapters 1 and 2 of “Artificial Intelligence - A Modern Approach”, by Stuart Russell and Peter Norvig.
  • ToDo
    • list of questions and confusions about the reading

Week 2: (Feb 13) Probability Review

  • Reading
  • ToDo
    • list of questions and confusions about the reading

Week 3: (Feb 20) Intro to Reinforcement Learning and MDPs

  • Reading
  • ToDo:
    • list of questions and confusions about the reading
    • think about networking problems we might formulate as MDP

Week 4: (Feb 27) Solving MDPs: Value Functions, Policy Iteration

  • Reading
  • ToDo:
    • list of questions and confusions about the reading
    • think about networking problems we might formulate as MDP

Week 5: (Mar 6) Reinforcement Learning recap

  • Reading
    • Chapter 21 of “Artificial Intelligence - A Modern Approach”, by Stuart Russell and Peter Norvig.
  • ToDo
    • list of questions and confusions about the reading
    • preliminary outline for overview paper

Week 6: (Mar 27) Solving MDPs: Value Iteration, Generalized Policy Iteration

Week 8: (April 3)

  • Reading
    • None: continue discussion from last week
  • ToDo
    • first draft of paper

Week 9: (April 10) Q-learning and Sarsa

  • ToDo
    • think about networking problems we might formulate as MDP

Week 10: (April 17) Routing and RL

Week 11: (April 27) Continue discussion from last week

Week 12: (May 1)

Week 12: (May 8)

General reference

ai_tutorial.txt · Last modified: 2017/04/27 17:05 by vmanfred