Course Catalog URL
Identifier
COMS 4720
Professor(s)
Last Updated: Spring 2025
Offered during Fall and Spring Semester each year.
- Credits and contact hours: 3 credits
- Instructor’s or course coordinator’s name: Yan-Bin Jia
- Text book, title, author, and year: Artificial Intelligence: A Modern Approach, 4th edition, Stuart Russell and Peter Norvig.
- Other supplemental materials: None
Specific course information
- Brief description of the content of the course: This course is about the design and implementation of intelligent agents---software or hardware entities that perform useful tasks with some degree of autonomy. The primary objective of is to provide an introduction to the basic principles and applications of Artificial Intelligence, including searching, learning, uncertainties, logic, and trust. Programming projects are used to help clarify basic concepts.
- Prerequisites or co-requisites: COM S 311, STAT 305 or STAT 330 or STAT 341, ENGL 250, Java programming experience
- Required, elective, or selected elective? Selected elective
Specific goals for the course
- Specific outcomes of instruction:
- Appreciation of fundamental problems in artificial intelligence (AI).
- Ability to generate precise formulation(s) of AI problems in terms of knowledge representation and search from imprecise English description(s).
- Ability to design intelligent agents for problem solving, reasoning, planning, decision making, and learning.
- Ability to make intelligent choices from among available algorithms and knowledge representation schemes subject to specific design and performance constraints, and when needed, design variants of existing algorithms.
- Ability to implement and evaluate intelligent agents for representative AI problems – e.g., automated theorem proving, learning classification rules from data, etc.
- Familiarity with some current applications of AI.
- Ability to communicate effectively about AI problems, algorithms, implementations, and their experimental evaluation.
Brief list of topics to be covered
- Introductions to Course and AI
- Intelligent Agents
- Search
- Adversarial Search
- Constraint Satisfaction
- Propositional Logic
- First-Order Logic
- Quantifying Uncertainty
- Bayesian Networks
- Machine Learning