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

COURSE INSTRUCTOR: Prof. Doug Downey

COURSE COORDINATOR: 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.

PREREQUISITES: EECS 325, EECS 111, or Lisp programming experience

DETAILED COURSE TOPICS:

  • 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.

LABORATORY PROJECTS:

GRADES:

  • 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

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