Artificial Intelligence
University of Texas at Dallas, Spring 2023
Instructors: Yunhui Guo
Time: TuTh 4:00PM - 5:15PM
Location: ECSS 2.201
Course Description: This course introduces the theoretical and computational techniques that serve as a foundation for the study of artificial intelligence (AI). Topics to be covered include the following:
- Introduction of AI and background: What is AI? Related fields
- Problem solving by search: principles of search, uninformed ("blind") search, informed ("heuristic") search, genetic algorithms, adversarial search
- Knowledge representation and reasoning: knowledge bases; logical and probabilistic inference; constraint satisfaction, planning, theorem-proving
- Game theory: games with hidden information, non-zero-sum games
Prerequisites:
CS3345 (Data Structures and Algorithms)
Prior programming experience is expected; familiarity with basic concepts of propositional logic and probability.
Required Textbooks and Materials:
No required textbook
Lecture slides will be available on the course website.
Suggested Course Materials:
Artificial Intelligence: A Modern Approach, Stuart Russell and Peter Norvig, Prentice Hall, Inc., second or third edition.
Course Grade: The course grade will be based on the following components.
- Homework assignments: 27% of the course grade
- Course project: 12% of the course grade
- Midterm 1: 18% of the course grade
- Midterm 2: 18% of the course grade
- Final exam: 25% of the course grade