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 be a CogSci major)] or be a Graduate student


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.

OPTIONAL 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

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