Note: This schedule will be adjusted based on the background and number of students enrolled in the class.
Note that in Winter quarter, there are two Monday holidays: Jan 18^th and Feb 15^th.
This schedule is subject to change, especially in the later parts of the quarter. In some ways, it also may be modified based on student preferences and background knowledge. Note items marked "TDB".

 

Week	Readings	Topics
Jan 4 & 6 Introduction and Background	Textbook, chapter 1 and chapter 2. First reaction paper due Jan 11: "Intelligence without Representation", Rodney Brooks, 1991.	What is artificial intelligence? Related fields: philosophy, psychology, mathematics, computer engineering, etc. A brief history of AI, including a discussion of the grand challenges (past and present) of the field. AI from a rational agent perspective. Agent architectures and programs.
Jan 11 & 13 Problem-Solving by Search	Textbook, chapter 3 and chapter 4. Also section 5.2 A Graded homework will be due on Jan 20th, for basic search algorithms that I do not have time to cover in class.	Principles of Search: Goal and problem formulation. Types 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. MiniMax search for game playing. Informed ("Heuristic") Search: Best-first, A*, iterative deepening A* (IDA*), and SMA*, search techniques. Heuristic functions. Search and optimization. Hill-climbing techniques.
Jan 20, 25, & 27 Logic, Knowledge Representation and Reasoning	Genesereth & Nilsson, chapter 2, pp. 9-36 (handout will be provided). Textbook, chapters 8, pp 217 - 234 and chapter 9, pp 265 - 286. Reaction paper due Jan 27: "Enabling Agents to Work Together", Guha and Lenat, 1994.	Knowledge representation and First-Order Logic. Inference, Unification & Theorem proving (including Prolog). Knowledge Bases and Knowledge-based systems. The CYC knowledge base.
Feb 1 & 3 Planning systems	Textbook, chapter 11, entire, plus section 12.1	STRIPS planners; partial-order plans Some time for midterm review
Feb 8 Midterm Exam
Feb 10/17 Probabilistic Knowledge Representation and Reasoning	Textbook, chapters 14 &  15, thru the beginning of 15.3 (p. 447) Also, another reaction paper on this topic (TBD), due Feb 17th	Review of Probability Theory: Conditional probability. Bayes' rule and its application. Probabilistic Reasoning with Belief Networks: Belief network semantics. Inference algorithms for singly-connected graphs. Practical issues in building belief networks. Decision-Theoretic Agents: Utility theory. Preferences and utility functions. Decision networks. Value of information. (Time permitting!)
Feb 22 & 24 Machine Learning	Textbook, chapters 18 thru 18.4 (p. 544) & 19 (entire) Reaction paper due Feb 24: "Induction of Decision Trees", Quinlan, 1986	Deductive vs. Inductive learning, prior knowledge, performance estimation. Learning logical descriptions. Probabilistic and statistical approaches. Classification, clustering, online learning, reinforcement learning. Neural networks.
Feb 24	Status report of final projects due
March 1 & 3 Natural Language Processing	Textbook, chapter 22 (entire) & section 23.2 only OR Rich & Knight, chapter 15	?? TBD: Russell and Norvig's approach is quite different from Rich & Knight, so I will choose one or the other (not both!).
March 8	This space intentionally left blank	Will be used either for other AI topics, or to make up slippage from this schedule.
March 10 Demos of final projects