CSC 242 Artificial Intelligence

Spring 2014

Instructor: Prof. Henry Kautz Office Hours: Friday 12:00 noon - 1:00pm, CSB 709
Grad TA: Xiaowan Dong Office Hours: 5:00-6:00pm Tuesday & Wednesday, CSB 724
Undergrad TAs:

Sean Esterkin
Daniel Scarafoni

Office Hours: 5:00-6:00pm Monday, 12:30-1:30pm Thursday, CSB 633 (CS Majors Lab)
Classroom Meliora 203  



Dishonesty: Claiming work by others as your own without attribution, unauthorized collaboration with other students. All cases will be referred to Academic Honesty Board.

Accommodations: I will make appropriate accommodations for students with learning differences. Inform me in writing within the first two weeks of class. Make an appointment to talk with me about differences that require accommodations beyond extra time on tests.


Programming: Projects must run on Linux on URCS instructional network. If you do not have an account (non-majors), contact grad TA Xiaowan Dong. Can use any language: Java, C, Python, LISP, Prolog, etc.


Project 1 Othello

Project 2 Planning as Satisfiabillity

Project 3 Face Recognition


date lecture slides assignments
21 Jan Lecture 01 - Introduction  
23 Jan Lecture 02 - Problem Solving Homework: Read Chapters 2 and 3 this weekend
28 Jan Lecture 03 - Search Strategies  
30 Jan Lecture 04 - Local Search Search Algorithm Demos. Demos are Java applets. If you are running Java 7 version 51 or newer, you must add this domain to Exception Site List (whitelist) in Java preferences in Settings or Control Panel.

Homework 01 Search posted. Solutions will be handed out in class on Thursday 6 February. You will need to know how to problems similar to these on the exams! Also, read Chapter 4 of AIMA.

4 Feb Lecture 05 - Adversarial Search  
6 Feb Lecture 06 - Alpha-Beta Pruning Project 1 Othello posted
11 Feb

Lesson 07 - Stochastic games & Constraint Satisfaction

Homework 02 Games posted
13 Feb Lecture 08 Propositional Logic  
18 Feb Lecture 09 Propositional Inference Homework 02 solutions given out in class
20 Feb Exam 1: Search  
25 Feb Lecture 10 First-Order Logic  
27 Feb Lecture 11 First-Order Inference

Othello Phase I due

Homework 03 posted

4 Mar Planning I

ULW Outlines due (hardcopy)

6 Mar Planning II

Homework 03 solutions given out in class

  March Break  
18 Mar Exam 2: Logic
20 Mar Probabilistic Reasoning

Othello Phase II and Othello Phase III (Optional) due

Project 2 Planning posted

Homework 04 Probability & Learning I posted

25 Mar Bayesian Networks  
27 Mar Learning & Approximate Inference in Bayesian Networks  
1 Apr (Class cancelled) ULW First Draft due (hardcopy)
3 Apr Learning from Examples Homework 04 solutions are in the box outside of CSB 709
8 Apr Exam 3: Probability & Learning I

Project 2 due

10 Apr

Neural Networks I

Project 3 Neural Networks posted

Reading on Blackboard Reserves: Chapter 4: Artificial Neural Networks, in T. Mitchell, Machine Learning, McGraw-Hill, 1997.

15 Apr

Neural Networks II

17 Apr Workshop  
22 Apr Reinforcement Learning  
24 Apr

Reinforcement Learning

(Same slides as previous lecture)

Homework 05 Learning and All Course Review posted. Solutions with not be given out for this assignment. You should complete all the problems in order to prepare yourself for the final exam.
29 Apr Wrap Up and Review

Project 3 due

ULW Final Draft due (hardcopy)

9 May 4:00-6:00pm Final Exam Learning II + all previous material