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Spring 2015 Syllabus

Week Day(mm/dd) Topic Slides Video Assignment Due(mm/dd)
1 2/6-2/12 Introduction to AI
  • Overview
  • Agents: Perception, Decisions, and Actuation
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Lec 1  
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Python Refresher (ungraded)

2 2/13-2/19 Search and Planning
  • Uninformed Search (Depth-First, Breadth-First, Uniform-Cost)
  • Informed Search (A*, Greedy Search)
  • Heuristics and Optimality

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Lec 2
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Lec 3
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HW1: Search  2/22

3 2/20-2/26 Project 1: Search and Planning P1: Search 3/1

4 2/27-3/5 Constraint Satisfaction Problems
  • Backtracking Search
  • Constraint Propagation (Forward Checking, Arc Consistency)
  • Exploiting Graph Structure

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Lec 4
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Lec 5 
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360p

HW2: CSPs 3/9

5 3/6-3/12 Game Trees and Decision Theory
  • Game Trees and Tree-Structured Computation
    • Minimax, Expectimax, Combinations
    • Evaluation Functions and Approximations
    • Alpha-Beta Pruning
  • Decision Theory
    • Preferences, Rationality, and Utilities
    • Maximum Expected Utility

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Lec 6
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Lec 7
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Other
Subtitle
Step-By-Step

HW3: Games 3/15

6 3/13-3/19 Project 2: Game Trees and Decision Theory P2: Multi-Agent
Games
 3/22

7 3/20-3/26 Markov Decision Processes (MDPs)
  • Policies, Rewards, and Values
  • Value Iteration
  • Policy Iteration

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Lec 8 
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Lec 9
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Subtitle

HW4: MDPs 3/29

8 3/27-4/2 Reinforcement Learning (RL)
  • TD/Q Learning
  • Exploration
  • Approximation

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Lec 10
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Lec 11
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Subtitle

HW5: RL 4/5

9 4/3-4/9 Project 3: Reinforcement Learning (RL) P3: RL 4/12

10 4/10-4/16 Conclusion and Wrap-Up Practice Finals
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III
 (ungraded)

11 4/17-4/23 No Lecture, Final Exam Week Final 4/26