Course overview

Tentative schedule (subject to change)

All lecture notes here just for references (login with RCS ID and password). All slides are only provided for references. Students are responsible for coming to class and taking their own notes.

Week Date Topic Book chapter

Slides

(Only for reference)

Project/Homework/Video
1 1-13

Introduction to the course

Uninformed Search

Chapter 1, 2

pdf ppsx

 

Sign up on piazza

Sign up on OPRA and

  1. vote on Lirong's OHs
  2. vote on TAs and mentors ' OHs

(must be on campus or use VPN)

1-16

Uninformed Search (BFS, DFS, Greedy)

Informed Search (A*)

3.4-3.4.3; 3.4.5

pdf ppsx

 
2

1-20 MLK day no class

Written HW 0 and Project 0 due by midnight

       

1-23

Add deadline 1-24

Informed Search (A*)

3.4.4; 3.4.6; 3.4.7; 3.5-3.5.2; 3.6 pdf ppsx  
3 1-27

Informed Search (A*)

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

 

1-30

Project 1 due by midnight

Alpha-beta pruning, Expectimax search

5

pdf ppsx

pdf ppsx

 
4 2-3

Constraint Satisfaction Problems

6 pdf ppsx  

2-6

Bayesian network 1: definition, conditional independence

14.1-14.3 pdf ppsx  
5

2-10

Project 2 due by midnight

Bayesian network 1: definition, conditional independence

14.1-14.3 pdf ppsx  

2-13

Bayesian network 2: inference, variable elimination 14.4-14.5 pdf ppsx  
6

2-18, following Monday schedule

Recap for midterm 1

     

2-20

In-class Exam 1

     
7

2-24

Written HW 1 due by midnight

Utility

Markov Decision Processes (MDPs)

16.1-16.3

21

pdf ppsx

pdf ppsx

 

2-27

Reinforcement learning

21 pdf ppsx  
8

3-2

Reinforcement learning 15.2, 15.5 pdf ppsx  

3-5 Written HW2 due by midnight

(3-6) drop deadline

Probabilistic Reasoning over Time

15.2, 15.5

pdf ppsx

pdf ppsx

 
9 (no class, spring break)

3-9

       

3-12

 

       
10 (no class, spring break) 3-16        

3-19

     

 

11

3-23 Project 3 due by midnight

Hidden Markov Models: Filtering Algorithm

Recap for Exam 2

15.2, 15.5, 15.6

pdf ppsx

Part 1: Exam 2 info

Part 2: Markov Models

Part 3: HMM

Part 4: Filtering Algorithm

3-26

Take-home Exam 2      
12

3-30

Hidden Markov Models: Particle Filters

  pdf ppsx

Part 1: filtering example

Part 2: sampling

Part 3: particle filtering alg

4-2 Project 4 due by midnight

Speech

Hidden Markov Models: Viterbi Algorithm

  pdf ppsx

Part 1: Speech

Part 2: Viterbi

Part 3: clarificaiton on m1

13

4-6

Naive Bayes

 

pdf ppsx

Part 1: intro to ML

Part 2: inference

Part 3: parameter estimation

4-9

Perceptrons,

 

pdf ppsx

 

Part 1: binary perceptron

Part 2: muti-class perceptron

14

4-13

MIRA, SVM, and k-NN   pdf ppsx

Part 1: MIRA

Part 2: SVM

Part 3: kNN

4-16 Social Choice   pdf ppsx

Part 1: scoring rules

Part 2: WMG

Part 3: axioms

15

4-20

Game Theory

  pdf ppsx

Part 1: games

Part 2: NE

Part 3: computation

4-23

Mechanism Design

  pdf ppsx

Part 1: framework

Part 2: 2nd price auctions

Part 3: VCG

16

4-27

Project 5 due by midnight

 

In-class Exam 3      

 

 

       
 

 

       
         

 

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!