CS 580-003
Introduction to Artificial Intelligence
Meets
Monday 1:30 pm - 4:10 pm Arlington: Van Metre Hall 466
Professor
Zoran Duric.
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 OR CS530 and CS531, no exceptions.
Textbooks
- Artificial Intelligence: A Modern Approach, 4th ed.,
Russell & Norvig (recommended), Prentice Hall
- Artificial Intelligence, Poole & Mackworth, 2nd ed., Cambridge
University Press (recommended), available online
Software
We will use Python for homework assignments and projects.
Course Web Page
We will communicate through
blackboard. Slides,
handouts, and assignments will be posted on the blackboard course
page. We will answer questions on blackboard.
Grading
Grading will be based on a combination of the following factors:
- Projects: 20%
- Homeworks: 10%
- Quizzes: 10%
- 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.