CSE440: Introduction to Artificial Intelligence

Fall 2017

Time 10:20-11:40am, Monday and Wednesday
Location: 228 Erickson Hall 
Professor: Joyce Chai, 2138 Engineering Building, 517-432-9239, jchai AT cse DOT msu DOT edu
Office Hours: Monday and Wednesday 1:30-3:00pm, or by appointment
Textbook: Artificial Intelligence: A Modern Approach (3rd Edition)  by Stuart Russell and Peter Norvig, Prentice Hall, 2010
TA: Drew Murray, murraydr@msu.edu
TA Office Hours: Monday 4:30-6:30 and Thursday 2:30-4:30 at Anthony Hall 3211

Course Description:

This is an introduction course to artificial intelligence covering fundamental topics in problem solving, heuristic search, knowledge representation, inference, planning, probabilistic reasoning, learning, and natural-language processing. 

Course Grades:

Six written homework assignments 30%
Two programming assignments 20%
Midterm 20%
Final Exam 30%

Homework and Examinations:

The work in this course consists of six written homework assignments, two programming projects, one midterm exam, and one final exam. The written assignments must be turned in at the beginning of the lecture on the day it is due. The programming projects are due before the midnight of the due date (through handin facility). No late homework will be accepted. Exams will be close book. There will be NO make-up exams except under extremely exceptional circumstances which must be documented and discussed with the professor ahead of time.  

Due date
Homework 1: September 13
Homework 2: September 27
Homework 3: October 11
Homework 4: November 1
Homework 5: November 15
Homework 6: November 29
Programming assignment 1 October 18
Programming assignment 2 December 4
Midterm October 23 (Monday) 7:00-8:20pm, EB1345
Final Exam: Friday, December 15, 7:45-9:45 a.m. Giltner Hall 146

Tentative Schedule of Topics

Topic Reading
Aug. 30 Introduction  Chapter 1
Sept. 6 Intelligent Agent Chapter 2
Sept. 11, 13 Search and Game Chapter 3, Chapter 5.1-3
Sept. 18, 20 Constraint Satisfaction Chapter 6.1-5
Sept. 25, 27 Logic and Logic-based inference Chapter 7, Chapter 8.1-4, Chapter 9.1-5
Oct. 2, 4 Prolog, DCG, and Parsing in Prolog Programming in Prolog: Chapter 1-4, Chapter 9
Oct. 9, 11 Planning, Knowledge Representation, and midterm review Chapter 10.1-2, Chapter 12.1-3
Oct. 16, 18 Uncertainties and Probabilities Chapter 13
Oct. 23, 25 Midterm Exam (moved to 7:00pm in EB1345), Bayesian Reasoning
Oct. 30, Nov. 1 Bayesian Network Chapter 14.1-5
Nov. 6, 8 Supervised Learning, Decision Tree Chapter 18.1-3
Nov. 13, 15 Concept Learning, Perceptron Learning 19.1-2
Nov. 20, 22 Neural Network and Deep Learning Chapter 18.7
Nov. 27, 29 Markov Decision Process Chapter 17.1-3
Dec. 4, 6 Ethical issues in AI, Honors projects presentation

Course materials are here for your reference. 

Academic Honesty:

Your grade should reflect your own work. Copying or paraphrasing someone's work (code included), or permitting your own work to be copied or paraphrased, even if only in part, is not allowed, and will result in an automatic grade of 0 for the entire assignment in which the copying or paraphrasing was done. Please talk to the instructor if you have trouble completing an assignment.

Alternative Testing:

Alternative testing is available to those with a documented disability affecting performance on tests. Students with documented disabilities requiring some form of accommodation receive a Verified Individualized Services and Accommodations (VISA) document which displays verified testing accommodations when appropriate. Please visit Alternative Testing Guidelines if applied. 

Notes: The instructor reserves the right to modify course policies and the course calendar according to the progress and needs of the class.