Principles of Artificial Intelligence

Course
Course Catalog URL: 
http://catalog.iastate.edu/azcourses/com_s/
Identifier: 
COM S 4720
Professor(s): 
Yan-Bin Jia

Last Updated: Fall 2024

Offered during Fall Semester each year.

  1. Credits and contact hours: 3 credits
  2. Instructor’s or course coordinator’s name: Yan-Bin Jia
  3. Text book, title, author, and year: Artificial Intelligence: A Modern Approach, 4th  edition, Stuart Russell and Peter Norvig.
  4. Other supplemental materials: None

Specific course information

  1. Brief description of the content of the course: The primary objective of this course is to provide an introduction to the basic principles and applications of Artificial Intelligence. Programming projects are used to help clarify basic concepts. The emphasis of the course is on teaching the fundamentals, and not on providing a mastery of specific commercially available software tools or programming environments. In short, this course is about the design and implementation of intelligent agents---software or hardware entities that perform useful tasks with some degree of autonomy. Upon successful completion of the course, students will have an understanding of the basic areas of artificial intelligence including problem solving, knowledge representation, reasoning, decision making, planning, and learning -- and their applications (e.g., game playing, text classification, medical diagnosis, data mining, information retrieval). Students will also be able to design and implement key components of intelligent agents of moderate complexity and evaluate their performance.
  2. Prerequisites or co-requisites: COM S 311, STAT 305 or STAT 330 or STAT 341, ENGL 250, Java programming experience
  3. Required, elective, or selected elective? Selected elective

Specific goals for the course

  1. 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