ICS 270A: Introduction to Artificial Intelligence

• When: Tuesdays and Thursdays, 3:30 to 5.
• Where: CS 253
• Professor: Padhraic Smyth
• Email: smyth@ics.uci.edu
• Office Location: CS 414E
• Office Hours: Tuesdays, 10 to 12.

COURSE OVERVIEW:

SYLLABUS:

1. Introduction and Background

   What is artificial intelligence (AI)? AI from a rational agent perspective. Related fields: philosophy, psychology, mathematics, computer engineering, etc. Review of the history of AI. Rational action and rational agents. Autonomous agents. Agent architectures and programs.

2. Problem-Solving by Search

   • Principles of Search

     Goal and problem formulation. Searching for solutions. Types of search problems. Components of search problems. Abstraction.

   • Uninformed ("Blind") Search

     Breadth-first, depth-first, uniform-cost, depth-limited, iterative-deepening, and bidirectional search techniques. Constraint satisfation problems. Time-space complexity. Completeness and optimality.

   • Informed ("Heuristic") Search

     Best-first, A*, iterative deepening A* (IDA*), and SMA*, search techniques. Heuristic functions. Search and optimization. Hill-climbing techniques.

   • Game Playing

     Two player game trees, decision making with perfect and imperfect information, minimax principle, evaluation functions, search cutoff strategies, alpha-beta pruning, performance of alpha-beta, state-of-the-art in game-playing programs.

3. Logical Knowledge Representation and Reasoning

   • Propositional Logic

   • First-Order Logic

   • Knowledge Bases

   • Inference

4. Probabilistic Knowledge Representation and Reasoning

   • Review of Probability Theory

     Axioms of probability. Conditional probability. Bayes' rule and its application.

   • Probabilistic Reasoning with Belief Networks

     Belief network semantics. Inference algorithms for singly-connected graphs. Inference in junction trees. Practical issues in building belief networks.

   • Decision-Theoretic Agents

     Utility theory. Preferences and utility functions. Decision networks. Value of information.

5. Learning

   • General Principles

     Representation, estimation. Inductive learning, prior knowledge, performance estimation. Learning logical descriptions. Probabilistic and statistical approaches.

   • Learning Problems and Solutions

     Classification, function approximation, clustering, online learning, reinforcement learning. Learning with trees, neural networks, memory-based systems, statistical models.

6. Agents in the Real-World

   • Vision and Speech

     Review of image processing and analysis techniques. Extracting information from images. Basic principles of speech recognition systems.

   • Natural Language

     Grammars and their applications. Parsing algorithms. Stochastic models for handling ambiguity.

   • Planning

     Planning problems and general solutions. Planning representations. Partial-Order Planning.

TEXTS

HOMEWORK, EXAMS, GRADING:

• Homeworks

  • Bi-weekly homeworks, handed out Thursday in class, due at the beginning of class the following Thursday (hand them in at the start of class).

  • No late homeworks, solutions will be discussed in class after homework is handed in.

  • General discussion of homework problems with classmates allowed, details of problem must be worked out individually.

  • Important!

    Homework solutions should be clear and to the point: you need to clearly convince me that you understand the solution.

• Computer Assignments

  There will be two or three computer assignments/projects during the quarter. You can use whatever programming language you wish, although C or C++ is preferred. Reports will be due Thursday at the start of class on the relevant week. You can hand in the report late, but will be graded out of 80%, 60%, etc., for every 1, 2, etc days that the report is late.

• Exams

  Midterm and final

• Grading

  Final grades will be a monotonic function of the sum of 30% of your homeworks, 30% of your computer assignments, and 40% of your midterm and final exams.

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