EECS 348: Intro to Artificial Intelligence

Quarter Offered

Fall : 12:30-1:50 TuTh ; Hammond
Spring : 12:30-1:50 TuTh ; Sood


EECS 111


Core techniques and applications of artificial intelligence. Representation retrieving and application of knowledge for problem solving. Hypothesis exploration, theorem proving, vision and neural networks.

  • This course satisfies the AI Breadth & AI Depth Requirement.

REQUIRED TEXTBOOK: Russell & Norvig , Artificial Intelligence: A Modern Approach , Prentice Hall, 3rd edition

COURSE INSTRUCTOR: Prof. Kristian Hammond (Fall), Prof. Sara Owsley Sood (Spring)

COURSE COORDINATOR: Prof. Chris Riesbeck

COURSE GOALS: The goal of this course is to expose students to the basic ideas, challenges, techniques, and problems in artificial intelligence. Topics include strong (knowledge-based) and weak (search-based) methods for problem solving and inference, and alternative models of knowledge and learning, including symbolic, statistical and neural networks.



  • Philosophical foundations of artificial intelligence
  • Intelligent agents
  • Search, including A*, iterative deepening
  • Logical formalisms, propositional and first order predicate calculus
  • Planning, from STRIPS to Partial Order Planning
  • Probability & uncertainty, including Bayesian inference and Bayes networks
  • Machine learning, including decision trees, neural nets, hill climbing, genetic algorithms

HOMEWORK ASSIGNMENTS: Varies, but always involves at least 3 major programming assignments, plus readings and/or papers.



  • Homeworks 50%
  • Exams 40%
  • Participation and extra credit 10%

COURSE OBJECTIVES: After this course, students should be able to

  • Articulate key problems, both technical and philosophical, in the development of artificial intelligence
  • Teach themselves more about AI through reading texts and research articles in the field
  • Apply AI techniques in the development of problem-solving and learning systems