ECE 479

Principles of Artificial Intelligence
Fall
Designation: 
Elective
Catalog Data: 

ECE 479 - Principles of Artificial Intelligence (3 units)

Description: Introduction to problems and techniques of artificial intelligence (AI). Topics inlcude automated problem solving, methods and techniques; search and game strategies; knowledge representation using predicate logic; structured representations of knowledge; automatic theorem proving, system entity structures, frames and scripts; robotic planning; expert systems; and implementing AI systems.

Grading: Regular grades are awarded for this course: A B C D E

May be convened with ECE 579

Prerequisite(s): 
ECE 373
Textbook(s): 

Russell, Stuart, and Peter Norvig. Artificial Intelligence: A Modern Approach. 3rd ed. Pearson, 2009.

Course Learning Outcomes: 

By the end of this course, the student will be able to:

  1. Demonstrate the ability to solve combinatorially complex problems by using heuristic techniques
  2. Construct knowledge representations and apply them as the foundation for design and analysis of complex computer-based systems
  3. Demonstrate an understanding of planning techniques, construct plans and plan-generating systems
  4. Design knowledge-based systems
  5. Design and implement reasoning engines and theroem provers
Course Topics: 

What is artificial intelligence?

Problems and problem spaces

  • State space search
  • Production systems
  • Control strategies
  • Heuristic search

Basic problem-solving methods

  • Forward and backward reasoning
  • Problem trees and graphs
  • The role of representation
  • Search methods

Game strategies

  • Minimax
  • Alpha beta search

Knowledge representation (KR)

  • Principles of KR using predicate logic
  • Overview of KR using other logics
  • Structured representations of knowledge

Planning   

  • Blocks world problems
  • Representation for planning
  • Plan generating systems

Advance topics including, but not limited to:

  • Computer-guided surgery
  • Intelligent sensing systems
  • Coevolution
  • Game theory
  • Big data science
Class/Laboratory Schedule: 

Two 75-minute lectures per week

Relationship to Student Outcomes: 

ECE 479 contributes directly to the following specific electrical and computer engineering student outcomes of the ECE department:

  • Ability to apply knowledge of mathematics, science and engineering (medium)
  • Ability to design and conduct experiments, as well as to analyze and interpret data (medium)
  • Ability to design a system, component or process to meet desired needs within realistic constraints, such as economic, environmental, social, political, ethical, health and safety, manufacturability and sustainability (medium)
  • Ability to function on multidisciplinary teams (low)
  • Ability to identify, formulate and solve engineering problems (high)
  • Understanding of professional and ethical responsibility (low)
  • Ability to communicate effectively (medium)
  • Broad education necessary to understand the impact of engineering solutions in a global, economic, environmental and societal context (medium)
  • Recognition of the need for, and an ability to engage in, life-long learning (medium)
  • Knowledge of contemporary issues (medium)
  • Ability to use the techniques, skills and modern engineering tools necessary for engineering practice (high)
Prepared by: 
Jerzy Rozenblit
Prepared Date: 
3/9/16

University of Arizona College of Engineering