An introduction to the theory and techniques underlying the design of intelligent computer systems. Topics include intelligent agents, problem representation, solving problems through search, game-playing, knowledge representation, reasoning, and planning, machine learning, and decision making under uncertainty.
Additional Requirements for Graduate Students: Graduate students will work on advanced project oriented research assignments that focus on a particular aspect of AI, and then report on their findings.
Athena Title
Artificial Intelligence
Prerequisite
[(CSCI 2610 or CSCI 2610E or CSCI 2611 or PHIL 2500) and (CSCI 1300-1300L or CSCI 1301-1301L or CSCI 1301E)] or permission of department
Semester Course Offered
Not offered on a regular basis.
Grading System
A - F (Traditional)
Student learning Outcomes
Students will model decision-making environments, consider factors such as observability, determinism, and uncertainty, and explain how these affect the design and performance of intelligent agents.
Students will analyze different search algorithms and strategies (uninformed, informed, local search) according to multiple performance criteria, including completeness, optimality, and complexity.
Students will apply appropriate search techniques and heuristics to solve pathfinding, optimization, and constraint satisfaction problems.
Students will utilize logic and probabilistic knowledge representation formalisms and inference algorithms to solve goal-oriented problems.
Students will apply and evaluate the effectiveness of machine learning techniques, including supervised, unsupervised, and reinforcement learning, to perform classification, regression, clustering, and decision-making tasks.
Students will explain contemporary issues raised by artificial intelligence research and different positions taken on these issues.
Topical Outline
What is Artificial Intelligence?
Intelligent Agent Design
Problem Solving
- Uninformed and Informed Search
- Metaheuristic Search and Optimization
- Adversarial Search
- Constraint Satisfaction
Knowledge, Reasoning, and Planning
- Knowledge-Based Agents
- Inference in Propositional and First Order Logic
- Planning
Machine Learning
- Supervised and Unsupervised Learning
- Neural Networks and Deep Learning
- Generative Models
Reasoning with Uncertainty
- Probabilistic Decision Making
- Reinforcement Learning
Institutional Competencies Learning Outcomes
Analytical Thinking
The ability to reason, interpret, analyze, and solve problems from a wide array of authentic contexts.
