CSE 480/580: Introduction to Artificial Intelligence

Course Description

The class will cover fundamental concepts, principles, and techniques in Artificial Intelligence.

Office Hours

Tuesday, Thursday: 4:30 pm - 5:30 pm and by appointment

Topics

Textbook

The course content will be based on the following textbook:

Grading Criteria

Attendance

Attendance is highly recommended but not mandatory. It is your responsibility to obtain any information given out in class. The instructor does not give out class notes. Some material presented in the lecture is not covered by the text. Students with special needs (e.g. hearing or vision difficulties) should inform the instructor at the beginning of the semester.

Honor Code

Please refer to the statement on academic integrity given below.

By attending Old Dominion University you have accepted the responsibility to abide by the honor code. If you are uncertain about how the honor code applies to any course activity, you should request clarification from the instructor. The honor code is as follows:

“I pledge to support the honor system of Old Dominion University. I will refrain from any form of academic dishonesty or deception, such as cheating or plagiarism. I am aware that as a member if the academic community, it is my responsibility to turn in all suspected violators of the honor system. I will report to Honor Council hearings if summoned.”

In particular, submitting anything that is not your own work without proper attribution (giving credit to the original author) is plagiarism and is considered to be an honor code violation. It is not acceptable to copy source code or written work from any other source (including other students), unless explicitly allowed in the assignment statement. In cases where using resources such as the Internet is allowed, proper attribution must be given.

Any evidence of an honor code violation (cheating) will result in a 0 grade for the assignment/exam, and the incident will be submitted to the Department of Computer Science for further review. Evidence of cheating may include a student being unable to satisfactorily answer questions asked by the instructor about a submitted solution. Cheating includes not only receiving unauthorized assistance, but also giving unauthorized assistance.

Students may still provide legitimate assistance to one another. You are encouraged to form study groups to discuss course topics. Students should avoid discussions of solutions to ongoing assignments and should not, under any circumstances, show or share code solutions for an ongoing assignment.

Please see the ODU Honor Council’s webpage for other concrete examples of what constitutes cheating, plagiarism, and unauthorized collaboration. All students are responsible for knowing the rules. If you are unclear about whether a certain activity is allowed or not, please contact the instructor.

Slides

Lecture1: Introduction

Lecture2: Intelligent Agents

Lecture3: Problem Solving Agents

Lecture4: Uninformed Search

Lecture5: Informed Search

Lecture6: Local Search

Colloqium

Lecture7: Evolutionary Computing

Lecture8: Genetic Algorithms

Lecture9: Applications of Genetic Algorithms

Lecture 10: Constraint Satisfaction Problem

Lecture 11: Backtracking

Lecture12: Games

Lecture13: Pruning

Lecture14: Midterm Sample Questions

Lecture15: Knowledge Based Agents

Lecture16: Inference - Part 1

Lecture17: Inference - Part 2

Lecture18: First Order Logic

Lecture19: Learning

Lecture20: Decision Trees

Lecture21: SVM

Lecture22: Linear Regression

Lecture23: Artificial Neural Networks

Special Lecture Reinforcement Learning