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Sept 7: Assignment 2 is now complete.

Aug 31: Assignment 1 now includes another example.

Lecture videos are available from the Canvas site (in the menu on the left) by selecting Echo 360.

To use jupyter notebooks on our CS department machines, you must add this line to your .bashrc file:

export PATH=/usr/local/anaconda/bin:$PATH


Week Topic Material Reading Assignments
Week 1:
Aug 21 - Aug 25
What is AI? Promises and fears.
Python review.
Problem-Solving Agents.
01 Introduction to AI
02 Introduction to Python
03 Problem-Solving Agents
Chapters 1, 2, 3.1.
AI, People, and Society, by Eric Horvitz.
Automated Ethics, by Tom Chatfield.
The Great A.I. Awakening, by Gideon Lewis-Krause, NYT, Dec 14, 2016.
"Fundamental Existential Threat": Lawmakers Warned of the Risks of Killer Robots, by Julia Conley
Section 1 of Scipy Lecture Notes
Week 2:
Aug 28 - Sept 1
Problem-solving search and how to measure performance.
Iterative deepening and other uninformed search methods.
04 Measuring Search Performance
05 Iterative Deepening and Other Uninformed Search Methods
06 Python Implementation of Iterative Deepening
Sections 3.1 - 3.4


Week Topic Material Reading Assignments
Week 3:
Sept 4 - Sept 8
Informed search. A* search. Python classes, sorting, numpy arrays. 07 Informed Search
08 Python Classes
Rest of Chapter 3 A1 Uninformed Search due Tuesday, September 5th, at 10:00 PM.
Here are examples of good A1 notebooks: a, b, c, d, e, f, g
Week 4:
Sept 11 - Sept 15
A* optimality, admissible heuristics, effective branching factor.
Local search and optimization.
09 Heuristic Functions
10 Local Search
Chapter 4 A2 Iterative-Deepening Search due Thursday, September 14th, at 10:00 PM.
Week 5:
Sept 18 - Sept 22
Adversarial search. Minimax. Alpha-beta pruning. Stochastic games. 11 Adversarial Search Chapter 5
Week 6:
Sept 25 - Sept 29
Negamax, with pruning. 12 Negamax
13 Modern Game Playing
A3 A*, IDS, and Effective Branching Factor due Friday, September 29th, at 10:00 PM.


Week Topic Material Reading Assignments
Week 7:
Oct 2 - Oct 6
Introduction to Reinforcement Learning. 14 Introduction to Reinforcement Learning Chapter 21
Reinforcement Learning: An Introduction
Week 8:
Oct 9 - Oct 13
Reinforcement Learning for Two-Player Games.
Introduction to Neural Networks
15 Reinforcement Learning for Two-Player Games
16 Introduction to Neural Networks
Sections 18.6 and 18.7 A4 Negamax with Alpha-Beta Pruning and Iterative Deepening due Wednesday, October 11th, at 10:00 PM.
Week 9:
Oct 16 - Oct 20
More Neural Networks 17 More Introduction to Neural Networks
Week 10:
Oct 23 - Oct 27
Introduction to Classification. Bayes Rule. Generative versus Discriminative. Linear Logistic Regression. 18 Introduction to Classification A5 Reinforcement Learning Solution to Towers of Hanoi due Wednesday, October 25th, at 10:00 PM.


Week Topic Material Reading Assignments
Week 11:
Oct 30 - Nov 3
Classification with Neural Networks 19 Classification with Linear Logistic Regression
20 Classification with Nonlinear Logistic Regression Using Neural Networks
Project Proposal due Wednesday, November 1st, at 10:00 PM.
Week 12:
Nov 6 - Nov 10
Reinforcement Learning with Neural Networks.
Lecture and Chuck's office hours on Thursday are cancelled. He will be out of town.
21 Reinforcement Learning with a Neural Network as the Q Function
Week 13:
Nov 13 - Nov 17
Faster Reinforcement Learning A6 Neural Networks due Wednesday, November 15th, at 10:00 PM.
Nov 20 - Nov 24 Fall Break
Week 14:
Nov 27 - Dec 1
Constraint satisfaction. Min-conflicts Chapter 6.
A new iterated local search algorithm for solving broadcast scheduling problems in packet radio networks


Week Topic Material Reading Assignments
Week 15:
Dec 4 - Dec 8
Finals Week:
Dec 11 - Dec 15
Final Project notebook is due Tuesday, Dec 12th, 10:00 pm.
start.1510672083.txt.gz · Last modified: 2017/11/14 08:08 by anderson