Computer Science CS 480 / 002

Introduction to Artificial Intelligence

Meets

MW 3:00-4:15 pm in Merten Hall 1200

Office hours

Monday/Wednesday 1:00-2:30 pm and by appointment

Professor

About the Class

The course can be roughly divided in two parts: (i) Intelligence from computation including uninformed and informed search, adverserial search, constraint satisfaction, markov decison processes, and reinforcement learning; (ii) Intelligence from data including probailistic reasoning, and unsupervised and supervised machine learning methods.

Prerequisites

CS330 and CS310, no exceptions.

Textbooks

Artificial Intelligence: A Modern Approach, 4th ed., Russell & Norvig, Prentice Hall
Artificial Intelligence, Poole & Mackworth, 2nd ed., Cambridge University Press (recommended), available online

Software

We will use Python for homework assignments and projects. AIspace, and AISpace2 (see the book page)

Course Web Page

We will communicate through piazza. Slides, handouts, and assignments will be posted on the blackboard course page.

Grading

Grading will be based on a combination of the following factors:

Homework: 40%
Midterm: 25%.
Final: 35%.

Honor Code

The class enforces the GMU Honor Code, and the more specific honor code policy special to the Department of Computer Science. You will be expected to adhere to this code and policy.

Disabilities

If you have a documented learning disability or other condition which may affect academic performance, make sure this documentation is on file with the Office of Disability Services and come talk to me about accommodations.

Course Outcomes

1. A knowledge of basic uninformed and heuristic search techniques. 2. A knowledge of basic logic or probabilistic reasoning techniques. 3. A knowledge of basic machine learning techniques. 4. An ability to implement basic AI methods in Python. 5. An ability to identify and apply an appropriate AI method to a given problem.