Course overview

Schedule (subject to change)

Week Date Topic Book chapter Slides Project/Homework
1        

 

 

1-18

Introduction to the course

Uninformed Search

Chapter 1, 2

pdf ppsx

Project 0 python tutorial, due: 1-25

Sign up on piazza

Sign up on OPRA and vote on OHs

2

1-22

Written HW 0 due before class

Uninformed Search (BFS, DFS, Greedy)

Informed Search (A*)

3.4-3.4.3; 3.4.5

pdf ppsx

 

1-25

Project 0 due by midnight

Informed Search (A*)

3.4.4; 3.4.6; 3.4.7; 3.5-3.5.2; 3.6 pdf ppsx  
3

1-29

Add deadline 1-30

Informed Search (A*)

3.4.4; 3.4.6; 3.4.7; 3.5-3.5.2; 3.6 pdf ppsx  
2-1

Constraint Satisfaction Problems

6 pdf ppsx

 

4

2-5

Project 1 due by midnight

Alpha-beta pruning

5 pdf ppsx  
2-8

Expectimax search

5 pdf ppsx  
5

2-12

Probability

13.1-13.5 pdf ppsx  

2-15

Project 2 due by midnight

Bayesian network 1: definition, conditional independence

14.1-14.3 pdf ppsx  
6

2-20, following Monday schedule

Bayesian network 2: inference, variable elimination 14.4-14.5 pdf ppsx  
2-22 Written HW 1 due before the class

Utility

16.1-16.3 pdf ppsx  
7

2-26

Recap for midterm 1

17.1-17.3    
3-1

In-class Exam 1

     
8

3-5

Markov Decision Processes (MDPs)

21 pdf ppsx  

3-8

Reinforcement learning

21 pdf ppsx (3-9) drop deadline
9 (no class, spring break)

3-12

       

3-15

       
10

3-19

 

Reinforcement learning 15.2, 15.5 pdf ppsx  
3-22

Probabilistic Reasoning over Time

15.2, 15.5 pdf ppsx  
11

3-26

Project 3 due by midnight

Hidden Markov Models

15.2, 15.5, 15.6 pdf ppsx

 

3-29

Social Choice   pdf ppsx  
12

4-2

 

Game Theory

  pdf ppsx  
4-5

Mechanism Design

  pdf ppsx  
13

4-9

Written HW2 due by midnight

Hidden Markov Models: Particle Filters

  pdf ppsx  

4-12

Speech and Introduction to Machine Learning

  pdf ppsx  
14

4-16

Naive Bayes, Perceptrons

 

pdf ppsx

pdf ppsx

 

4-19

Project 4 due by midnight

MIRA, SVM, and k-NN

  pdf ppsx  
15

4-23

Recap      

4-26

 

In-class Exam 2      
16

4-30

       

5-3

Project 5 due by midnight

 

     
17

5-7

 

     
         

 

Textbook

Artificial Intelligence: A Modern Approach (third edition), Prentice Hall, 2009.

By Stuart Russell and Peter Norvig

Prerequisites

Objectives

General Class Policies

Grading

Project assignments and written homeworks

Academic dishonesty and late policy

Acknowledgements

Thanks Pieter Abbeel, Vincent Conitzer, John DeNero, Dan Klein, Malik Magdon-Ismail, Peter Sone for offering tremendous helps on developing the course!