CMSC 421
Introduction to Artificial Intelligence
Fall 2002

Approximate Schedule*


This schedule shows for each class session: the topics that will be covered, the required and optional readings and any assignments that are due that day. All homework and programming assignments are due at the beginning of class. THIS SCHEDULE WILL CHANGE, PLEASE CHECK IT REGULARLY!


Session Date Topic Notes Required
Reading
Optional
Reading
Assignment
slides**
1
Sep 3
Introduction and Course Overview  
ch. 1
 

Lecture1-notes
AI-Intro (ppt, pdf)

2
Sep 5
Intelligent Agents & Problem Solving  
ch. 2
 
Lecture2-notes
Agents and Problem Solving (ppt,pdf)
3
Sep 10
Search  
ch. 3
 
hw1 due at start of class Thu 9/24

Lecture3-notes
Uninformed Search(ppt,pdf)

4
Sep 12
Heuristic Search

 

ch. 4
 
Lecture4-notes
Heuristic Search(ppt,pdf)
5
Sep 17
Search cont.  
 
6
Sep 19
Constraint Satisfaction Problems (CSP) guest lecturer  
lecture (ppt,pdf)
7
Sep 24
CSP/Game Playing  
ch. 5
 
CSPII(ppt,pdf)
8
Sep 26
Game Playing  
 
hw2 due noon Fri 10/11
Game Playing(ppt,pdf)
9
Oct 1
KR & Propositional Logic  
ch. 6
 
pa1 due 9AM Tue 10/15
Propositional Logic(ppt,pdf)
10
Oct 3
Propositional Logic and Inference  
 
Propositional Inference(ppt,pdf)
11
Oct 8
Resolution & Theorem Proving  
 
12
Oct 10
First Order Logic (FOL)  
ch. 7
 
First Order Logic(ppt,pdf)
13
Oct 15
FOL & Theorem Proving, Review  
ch. 9
 
 
14
Oct 17
Midterm
15
Oct 22
Planning  
ch. 11
 
hw3 due noon Tue 11/5
PlanningI(ppt,pdf)
16
Oct 24
Planning
 
ch. 12
     
17
Oct 29
Planning      
PlanningII(ppt,pdf)
18
Oct 31
Reasoning Under Uncertainty  
Daphne Koller's notes on BNs (ps, pdf)
   
Uncertainty (ppt,pdf)
19
Nov 5
Bayesian Networks  
 
hw4 due noon Tue 11/19
Bayesian Networks(ppt,pdf)
20
Nov 7
Bayesian Networks  
     
21
Nov 12
Decision Making Under Uncertainty  
ch. 16
   
Decision Making(ppt,pdf)
22
Nov 14
Decision Making cont.  
     
23
Nov 19
Learning - Decision Trees  
ch. 18
   
LearningI(ppt,pdf)
24
Nov 21
Learning -Neural Networks  
ch. 19
   
LearningII(ppt,pdf)
25
Nov 26
Learning cont.  
     
26
Nov 28
thanksgiving
27
Dec 3
Reinforcement Learning  
ch. 20
   
LearningIII(ppt,pdf)
28
Dec 5
SNOW DAY
NO CLASS
 
     
29
Dec 10
Robotics  
ch. 25
   
Robotics(ppt,pdf)
30
Dec 12
Review  
     

Tuesday
Dec 17
8 AM - 10 AM

Final
       

 

Key:
RN = Russell & Norvig, Artificial Intelligence: A Modern Approach
H = homework assignment
P = project

* This schedule is subject to change. There are a number of readings that will be added, and the assignment dues dates are still being tuned. Please check it periodically.

** Lecture-notes are the daily announcements/reminders for each class. The lecture content is in the file with the appropriate topic name. The course slides are based on the course notes of Jean-Claude Latombe at Stanford, Marie desJardins at UMBC, Daphne Koller at Stanford and others.

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