Welcome to ITK 340: Introduction to Artificial Intelligence

Spring, 2009

Syllabus

Grades, Assignment Submission and More on Blackboard (login required)

UNIX Information Handout

Instructions for Using Exceed on Demand

Accessing the Suns without Exceed

179 UNIX Lab

Week Date Reading Assignment for this class Assignments due Topic
1 1/12 None None Introduction
1/14 Read Chapter 1 Chapter 1, exs 1 and 9 Foundations and History of AI
1/16 Read chapter 2

Chapter 1, exs 10-13
Email
Questionnaire

Agents
2 1/19 MLK Day, no class None None
1/21 Read chapter 3 Chapter 2, exs 5-6 Search
1/23 Read chapter 4, sections 1-2 Chapter 3, ex. 7 Search cont.
3 1/26 Finish chapter 4 None Heuristic Search
1/28 Read chapter 6, sections 1-3 Chapter 4, ex. 1 Local Search
Competitive Search
1/30 Finish chapter 6 Program 1, Milestone 1 Game search, cont.
4 2/2 Chapter 7, sections 1-4 Chapter 6, ex. 1 Propositional Logic
2/4 Finish chapter 7 Chapter 7, ex. 1 Propositional Inference
2/6 Chapter 8 Program 1, Milestone 2 First Order Logic
5 2/9 Chapter 9, sections 1-4 Chapter 7, exs. 8-9 First Order Logic cont.
2/11 Finish chapter 9 Chapter 8, ex. 6 Inference
2/13 None Program 1 Inference, cont.
6 2/16 http://www.coli.uni-saarland.de/~kris/learn-prolog-now/ "lectures" 1-5 Prolog Handout 1
Chapter 9, ex 4
Inference, cont.
Prolog
2/18 None Chapter 9, ex 9 Prolog
2/20 lectures 7-10 Prolog Handout 2 Prolog
7 2/23 None Take home portion of exam 1 Exam 1
2/25 None Prolog Handout 3 Prolog
2/27 Chapter 10, sections 1-2 Prolog Handout 4 Resolution
Knowledge Representation
8 3/2 Chapter 10, sections 3-5 Chapter 9, ex 18 Knowledge Representation
3/4 Finish chapter 10 Program 2 milestone Knowledge Representation
3/6 None Chapter 10, ex 5 (just the first -- don't do the repeat) Knowledge Rep, cont.
SPRING BREAK NO SCHOOL
9 3/16 Chapter 13 Chapter 10, ex. 22 (Note that this is a very challenging exercise) Probability
3/18 Chapter 14, sections 1-3 None Probability
Bayesian networks
3/20 Finish chapter 14 Chapter 13, exercises 6 and 8
Program 2 (postponed from Wednesday)
Bayesian networks
10 3/23 Chapter 11, sections 1-2 Chapter 14, exercises 1 (a-d) and 3 (a, b, d) Planning
3/25 Finish chapter 11 Chapter 11, exercise 4 Partial Order Planning
3/27 Chapter 18, sections 1-3 None Learning
11 3/30 Finish chapter 18 Chapter 18, exercises 1-2 Concept Learning
4/1 None None Decision tree learning
4/3 Chapter 19, sections 1-3 Program 3 Decision tree learning
12 4/6 Finish chapter 19 Chapter 18, exercises 3, 4, 7 Ensembles
Knowledge based learning
4/8 Read chapter 20, sections 1-2, 5 None Inductive Logic Programming
4/10 None Exam 2 Take-home Exam 2 (through chapter 18)
13 4/13 Chapter 20, sections 7-8 None Neural Nets
4/15 Chapter 22, sections 1-3 None Neural Nets
4/17 Chapter 22, sections 4-6 Program 4 Language Processing
14 4/20 Finish chapter 22 Chapter 22, exercises 1, 7, 9, 14 Language Processing, cont.
4/22 Chapter 23, sections 1-2 FOIL exercise Language Processing, cont.
4/24 Finish chapter 23 Chapter 23, exercises 8, 9 Language Processing cont.
15 4/27 Chapter 26 None Ethical and Philosophical Issues
4/29 None None Philosophy and AI
5/1 Chapter 27 Program 5 Wrap-up
Final Exam, Monday, May 4, 1:00 pm