Skip to this view's content
Help

Syllabus

This syllabus is subject to change. In particular, dates for unreleased materials are only estimates and are likely to change slightly.

Week Dates Topic Slides Videos Homework Due Project Due
1 2/18 - 2/24 Introduction to AI small
large
1: edX
1: .tar
Python Refresher Ungraded
   
  • Overview
  • Agents: Perception, Decisions, and Actuation

2 2/25 - 3/3 Search and Planning small
large
pseudo-code
2: edX
2: .tar
3: edX
3: .tar
step: edX step: .tar
HW1: Search 3/3
  • Uninformed Search (Depth-First, Breadth-First, Uniform-Cost)
  • Informed Search (A*, Greedy Search)
  • Heuristics and Optimality

3 3/4 - 3/10 Project 1: Search and Planning P1: Search 3/10

4 3/11 - 3/17 Constraint Satisfaction Problems small
large
4: edx
4: .tar
5: edx
5: .tar
HW2: CSPs 3/17
  • Backtracking Search
  • Constraint Propagation (Forward Checking, Arc Consistency)
  • Exploiting Graph Structure

5 3/18 - 3/24 Game Trees and Decision Theory small
large
6: edx
6: .tar
7: edx
7: .tar step: edX step: .mp4
HW3: Games 3/27
  • 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

6 3/25 - 3/31 Project 2: Game Trees and Decision Theory P2: Multi-Agent Games 4/7

7 4/1 - 4/7 Markov Decision Processes (MDPs) small
large
8: edx
8: .tar
9: edx
9: .tar
HW4: MDPs 4/10
  • Policies, Rewards, and Values
  • Value Iteration
  • Policy Iteration

8 4/8 - 4/14 Reinforcement Learning (RL) small
large
10: edx
10: .tar
11: edx
11: .tar
HW5: RL 4/17
  • TD/Q Learning
  • Exploration
  • Approximation

9 4/15 - 4/21 Project 3: Reinforcement Learning (RL) P3: RL 4/28

10 4/22 - 4/28 Conclusion and Wrap-Up Practice Final
Practice Final 2
(ungraded)
(ungraded)

11 4/29 - 5/5 No Lecture, Final Exam Week Final 5/14