CSCI-431: Artificial Intelligence

Spring 2012

 

This course provides an introduction to artificial intelligence. Topics include uninformed and informed (heuristic) search, adversarial search, reinforcement learning, Bayesian networks, Hidden Markov Models, machine learning algorithms, computer vision, natural language processing, and robotics. The course will include approximately four substantial programming assignments in Python. Co-requisite: CSCI 361 (Algorithms and Data Structures).

Contact Info

Instructor: David Akers
Lectures: Tu-Th 11:00-12:20 (TH 387)
Labs: Some Thursdays, class will be held in TH409 or TH420
Phone: 879-3126
Email: dakers@pugetsound.edu
Office: Thompson 600
Office Hours: Mon 3:00-3:50, Tues-Thurs-Fri: 2:00-2:50, and by appt.

Announcements:

Resources:

Schedule:

Week Topic Reading Quiz? Asmt Out Due
1/16 Introduction to AI, python
Chapter 1   Python Tutorial Friday 1/27
1/23 Agents and search
Ch. 3.1-3.6   P01 (Search) Monday 2/13
(checkpoint: Monday 2/6)
1/30 Heuristic-based search Review sections 3.4-3.6
     
2/6 Constraint satisfaction problems Ch. 6.1-6.5      
2/13 Adversarial search (search for games) Ch. 5.2-5.5 Quiz Thursday 2/16 on CSPs
(practice problems)
P02 (Multi-Agent) Wednesday 2/29 (last possible day to hand in: Monday 3/5)
2/20 Expectimax search, Markov Decision Processes (MDPs)
Sutton and Barto Ch. 3-4      
2/27 MDPs, Reinforcement learning Ch. 17.1-17.3   P03 (Reinforcement Learning) Thursday 3/29 (last possible day to hand in: Tuesday 4/3)
3/5 Reinforcement learning, cont'd Sutton and Barto Ch. 6.1, Ch. 2, Ch. 5 Quiz Tuesday 3/6 on MDPs
(practice problems)
   
3/12 Spring Break
3/19 Probability, Bayes Nets: representation and independence Ch. 13.1-13.5,
Ch. 14.1-14.2
     
3/26 Bayes Nets: inference and sampling Ch. 14.4.1,
Ch. 14.5.1
Quiz Tuesday 3/27 on probability
(practice problems)

(quiz solutions)
P04 (Ghostbusters) Sunday 4/22 (last possible
day to hand in: Wednesday 4/25)
4/2 Hidden Markov Models (HMMs) Ch. 15.1-15.3,
Ch. 15.5
Quiz Tuesday 4/3 on
Bayes Nets/Markov Models
(practice problems)
   
4/9 Machine learning (naive bayes, perceptron model) Review Ch. 15.5      
4/16 Machine learning, cont'd No new reading      
4/23 Computer vision, natural language processing, robotics

Take-home exam out Thursday, due Monday at 5:00 pm

No new reading No quiz    
4/30 Final thoughts
Last day is Tuesday.
No new reading      
5/7

Final exam week

There is no in-class final exam for this class.