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Undergraduate Programs
Home / Undergraduate Programs / Courses / Principles of Artificial Intelligence

ECE 479

Principles of Artificial Intelligence

Spring
Required Course:
No

Course Level

Undergraduate

Units

3

Prerequisite(s)

ECE 373

Course Texts

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

Schedule

150 minutes lecture per week

Course 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.

May be convened with ECE 579.

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

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)

Syllabus Prepared By

Jerzy Rozenblit, 03/09/16
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520.621.6193

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