EECS 348: Intro to Artificial Intelligence

Quarter Offered

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


EECS 111 and (EECS 214 or CogSci major)


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. Mike Rubenstein (Spring)

COURSE COORDINATOR: Prof. Kristian Hammond

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