CS 4600
Introduction to Intelligent Systems
Rough Schedule

This schedule provides an outline of what we will cover, indicates the readings from the book, and provides some sample homework problems for you to work on. Lectures may deviate significantly as we proceed in the course. Stay up to date with the class lectures, news, and announcements.

Date Topic Reading Out Due
January 6 Agents Chapter 2
January 8 Uninformed Search Chapter 3
January 13 Informed Search Chapter 4 Sample Problems 1 Solution 1
January 15 More Informed Search and CSP Chapter 4+5
January 20 Constraint Satisfaction, Hill Climbing, Simulated Annealing, Genetic Algorithms Parts of 3,4 and 5 Project 1 out
January 22 Game Playing, Minmax, Evaluation functions, Isolation Chapter 6 Sample Problems 2 Solution 2
January 27 More Game Playing, catch-up Chapter 6 Project 1 due
January 29 Alpha Beta, horizon effect, quiescence, Expectiminmax Chapter 6 Project 2 out
February 3 Isolation project thoughts, Beginning Logic Chapter 7
February 5 More Logic, Hunt the Wumpus Chapter 7
February 10 Review + more logic Chapter 7 Project 2 due
February 12 (drop day tomorrow) "Mid-term" (closed book)
February 17 Logical Agents; First Order Logic Chapter 7+8 Competition entries due
February 19 FOL Inference Chapter 8+9 Sample Problems 3 Solution 3
February 24 FOL Inference Chapter 9
February 26 Representation and Reasoning Chapter 10 Sample Problems 4 Solution 4
March 2 Planning Chapter 11
March 4 Planning+catch-up Chapter 11+12 Sample Problems 5 Solution 5, Project 3 due
March 9 Spring Break
March 11 Spring Break
March 16 Probability Chapter 13
March 18 Probability cont. Chapter 13
March 23 Bayes's Nets Chapter 14
March 25 Bayes's Nets cont. Chapter 14 + 15 Sample Problems 6 Solution 6
March 30 Hidden Markov Models Chapter 15
April 1 HMMs, language recognition (speech, sign, handwriting) In class notes (Chapter 15, parts 20, parts 23)
April 6 Recognition cont., cross-validation, vector quantization In class notes (Chapter 15, parts 20, parts 23)
April 8 PCA/LDA/ICA, minimum description length In class Project 4 due
April 13 k-nearest neighbors, maximum liklihood, MAP, generalization vs. overfitting In class + Chapter 20
April 15 k-nearest neighbors, maximum liklihood, MAP, generalization vs. overfitting In class + Chapter 20
April 20 Decision Trees Chapter 18 Sample Problems 7 Solution 7
April 22 MDP, POMDP, review Chapter 17 Sample Problems 8 Solution 8 Project 5 due
April 28 Weds. 8-10:50am Tenative Final (check Oscar for updates)