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. Changes may appear here, but will always be also sent via email to all enrolled students.

 

Week	Readings	Topics
Sept 28 & 30 Introduction and Background	Textbook, chapter 1 & chapter 2. First reaction paper due Oct 5: "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.
Oct 5 & 7 Problem-Solving by Search	Textbook, chapter 3 & chapter 4. Also section 5.2 An easy, but graded homework will be due on Oct 12th, 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.
Oct 12, 14, & 19 Logic, Knowledge Representation and Reasoning	Luger & Stubblefield, chapter 2, pp. 41-61 (handout will be provided). Textbook, chapters 7.1 (pp. 185-194) chapter 8, pp. 217-234 and chapter 9, pp. 265-286. Reaction paper due Oct 19: "Enabling Agents to Work Together", Guha and Lenat, 1994. Another easy graded homework due on Oct 21.	Knowledge representation and First-Order Logic. Inference, Unification & Theorem proving (including Prolog). Knowledge Bases and Knowledge-based systems. The CYC knowledge base. (Guest lecture by Prof. Pratt on 10/19)
Oct 21 & 26 Planning systems	Textbook, chapter 11, entire, plus section 12.1 Final project plan due on Oct 26.	STRIPS planners; partial-order plans Some time for midterm review
Oct 28 Midterm Exam
Nov 2 & 4 Probabilistic Knowledge Representation and Reasoning	Textbook, chapters 14 &  15, thru the beginning of 15.3 (p. 447) Also, another reaction paper on this topic: "Medical Expert Systems based on Causal Probabilistic Networks" (Andreassen, Jensen & Olesen, '91), due Nov. 4th	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. (Time permitting!)
Nov 9 & 11 Machine Learning	Textbook, chapters 18 thru 18.4 (p. 544) & 19 (entire) Reaction paper due Nov. 11:  "Induction of Decision Trees", Quinlan, 1986	Deductive vs. Inductive learning, prior knowledge, performance estimation. Learning logical descriptions. Probabilistic and statistical approaches. Classification, clustering, reinforcement learning, and neural networks. (Guest lecture by Prof. Lathrop on Nov 9.)
Nov 16	Status report of final projects due
Nov 16, 18, & 23 Natural Language Processing	Textbook, chapter 22 (entire) & sections 23.1 & 23.2 only	Speech Understanding, NL generation, NL understanding, NL Parsing. Message understanding, text retrieval. Grammars for NLP. Augmenting syntactic parsing with semantics.
Nov 30	This space intentionally left blank	Will be used either for other AI topics, or to make up slippage from this schedule. Also, some time for final exam reviews, and tips and an example PPT presentation.
Dec 2 Demos of final projects Final project reports due 5pm, Dec. 3
Dec 7 Final Exam, 4pm - 6pm.