CSE440: Introduction to Artificial Intelligence
|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, firstname.lastname@example.org|
|TA Office Hours:||Monday 4:30-6:30 and Thursday 2:30-4:30 at Anthony Hall 3211|
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.
|Six written homework assignments||30%|
|Two programming assignments||20%|
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.
|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
|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.
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 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.