ECE: Electrical & Computer Engineering
Accredited by ABET
Undergraduate Programs

ECE 4524 Artificial Intelligence and Engineering Applications

Fall 2016 textbook list

The Fall 2016 ECE textbook list is available online for students.

Current Prerequisites & Course Offering

For current prerequisites for a particular course, and to view course offerings for a particular semester, see the Virginia Tech Course Timetables.

Return to course list

ECE 4524 Artificial Intelligence and Engineering Applications (4C)

Problem solving methods; problem spaces; search techniques; knowledge representation; programming languages for AI; games; predicate logic; knowledge-based systems; machine learning; planning techniques; reactive systems; artifical neural networks; natural language understanding; computer vision; robotics.

What is the reason for this course?

Computational systems are very good at repetitive tasks that are completely specified in advance. They do not perform so well at tasks that are poorly specified. The course presents Artificial Intelligence as providing the potential for making computers more useful, motivated in part by biological systems. Engineering applications are used to illustrate the concepts.

Design Technical Elective for CPE; Technical Elective for EE. Typically offered: Fall. Program Area: Computers.

Prerequisites: 2574; STAT 4714.

Why are these prerequisites or corequisites required?

ECE 2574 is needed to ensure a modest level of programming ability. STAT 4714 is needed as a background for discussions of inductive inference and machine learning.

Department Syllabus Information:

Major Measurable Learning Objectives:
  • Formulate problems that involve knowledge representation and state-space search
  • Demonstrate proficiency with an AI programming language
  • Develop and analyze software that performs heuristic search
  • Develop and analyze software for game-playing using adversarial search
  • Use predicate logic to represent facts and perform resolution theorem proving
  • Develop algorithms and software for action planning, machine learning, natural language processing, and computer vision in constrained problem domains
  • Develop and analyze a knowledge-based ("expert") system
  • Describe the operation and limitations of common artificial neural network systems

Course Topics
Topic Percentage
What is artificial intelligence? 5%
Languages for Artificial Intelligence programming: Lisp, Prolog 10%
Problems and Problem Spaces 5%
Basic Problem Solving Methods and Heuristic Search 10%
Games 10%
Predicate Logic and Theorem Proving 10%
Knowledge Representation 5%
Knowledge-based (Expert) Systems 10%
Machine Learning 10%
Natural Language Understanding 10%
Computer Vision and Scene Analysis 5%
Planning Methods and Reactive Systems 10%

Return to course list