Dr. Collin Lynch
CSC 520 Artificial Intelligence I
3 Credit Hours
Introduction and overview of artificial intelligence. Elements of AI problem-solving techniques. State spaces and search techniques, including heuristic search (hill-climbing and A*). Logic (first-order predicate calculus) and theorem proving (unification, resolution theorem proving). Advanced topics in machine learning, reasoning under uncertainty (Bayesian reasoning), and natural language processing as time permits.
Undergraduate degree in computer science with courses in data structures (CSC 316) AND applied discrete mathematics (CSC 226) or background in symbolic logic. Note: CSC 226 and 316 are offered as a part of the Computer Programming Certificate and can be taken online to fulfill this prerequisite.
CSC 520 is the foundational artificial intelligence course. It is intended to prepare students for advanced courses in AI. A student successfully completing this course will be able to:
- Identify representations and methodologies useful in the development of computer-based systems which exhibit aspects of intelligent behavior. Design and implement intelligent agents to operate in simple environments;
- Identify the utility and limitations of knowledge representation methodologies such as propositional and predicate logic, rule-based systems, and probabilistic systems;
- Identify the utility and limitations of companion reasoning methods, including resolution, rule processing, probabilistic reasoning, machine learning, and natural language processing;
- Distinguish various uninformed and informed search algorithms and identify when each is appropriate;
- Design and implement a series of intelligent agents of increasing complexity.
HOMEWORK: Programming Assignments (2-4), Other (1-2)
EXAMINATIONS: Midterms and Final Exam
SOFTWARE REQUIREMENTS: Programming assignments use Java
Stuart Russell & Peter Norvig, Artificial Intelligence: A Modern Approach, 3rd Edition , Prentice Hall, 2010, ISBN 978-0-13-604259.