CSE440: Introduction to Artificial Intelligence

Fall 2016

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, Wednesday:  2:30-4:00pm, or by appointment
Textbook: Artificial Intelligence: A Modern Approach (3rd Edition)  by Stuart Russell and Peter Norvig, Prentice Hall, 2010
TA: Lanbo She, shelb26@gmail.com

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 1 15%
Midterm 2 15%
Final Exam 20%

Homework and Examinations:

The work in this course consists of six written homework assignments, two programming projects, two midterm exams, and one final exam. The written assignments must be turned in at the beginning of 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. The final exam will be comprehensive. 

Due date
Homework 1: September 14
Homework 2: September 28
Homework 3: October 17
Homework 4: October 31
Homework 5: November 16
Homework 6: November 30
Programming assignment 1 October 28
Programming assignment 2 December 5
Midterm 1 October 5 (Wednesday)
Midterm 2 November 9 (Wednesday)
Final Exam: Wednesday, December 14, 12:45-2:45 p.m.

Tentative Schedule of Topics

Topic Reading
Aug. 31 Introduction  Chapters 1
Sept. 7 Intelligent Agent, Search Chapter 2
Sept. 12, 14 More search Chapter 3
Sept. 19, 21 Games, Constraint Satisfaction Chapter 5.1-3, Chapter 6.1-5
Sept. 26, 28 Propositional Logic and FOL Chapter 7, Chapter 8.1-4
 Oct. 3, 5 Logic-based Inference and Midterm 1 Chapter 9.1-5
Oct. 10, 12 Prolog, DCG, and Parsing in Prolog Programming in Prolog, Chapter 1-4, Chapter 9
Oct. 17, 19 Planning, Knowledge representation,
Chapter 10.1.1-10.2.2, Chapter 12. 1-3,
Oct. 24, 26 Uncertainties, Bayes rules Chapter 13,
Oct. 31, Nov. 2 Bayesian networks   Chapter 14.1-5
Nov. 7, 9  Supervised Learning,  Midterm 2  Chapter 18.1-2
Nov. 14, 16 Decision Trees, Chapter 18.3
Nov. 21, 23 Neural Network, Concept learning, Chapter 18.7; 19.1-2
Nov. 28, 30  MDP  Chapter 17.1-3
Dec. 5, 7 NLP, Robotics Chapter 23, Chapter 25

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.