Foundations of Artificial Intelligence

CS4700 - Fall 2008
Cornell University
Department of Computer Science

The numbers in [] brackets are the relevant chapters or sections of the [Russell/Norvig] textbook.

The slides are organized in modules. Typically each module covers a chapter of the textbook, presented in one or more lectures..

Lecture	Date	Topic	Reading (R&N)	Slides	Comments
1,2	8/29 9/1	Course overview; What is AI?	[1]	Introduction	AI Timeline Artificial Intelligence - Alive and Kicking AI Reaches the Golden Years Take a Moment to Raise a Glass to the Wonderful, Underappreciated AI AI in the News
3	9/3	Structure of intelligent agents and environments	[2]	Agents
4,5	9/5 9/8	Problems and Problem Spaces Uninformed Search	[3]	Search1
6,7	9/10 9/12	Informed Search	[4.1; 4.2]	Search2	A* Wikipedia DIMACS Challenge: Shortest Path
8,9	9/15 9/17	CSP: Search and Inference	[5]	CSP1	CSP - Wikipedia Online guide to CP CP conference CP 2002 (Cornell)
10	9/19	CSP: Global Constraints, AllDiff	[5] A filtering algorithm for constraints of difference in CSPs, Regin 94 (see CMS)	CSP2
11,12	9/22 9/24	Adversarial search	[6]	Game Playing	Computer Beats Pro at US Go Congress
13,14	9/26 9/29	Local search	[4.3,5.3]	Local Search
15,16,17	10/1 10/3 10/6	Instance Hardness Randomization in Complete Search Search-Wrap-Up	[pages 224-225]	Instance Hardness Randomized Backtrack Search
18	10/8	Midterm
19	10/10	Knowledge-base Agents: Knowledge and Inference; Mine Sweeper and Wumpus World; Logic Entailment	[7]	Logic1
20	10/15	Propositional Logic: Syntax, Semantics	[7]	Logic2
21/22	10/20 10/22	Propositional Logic: Inference	[7]	Logic3
23/24	10/24 10/27	Sat Encodings and Sat Solvers Learning in Sat	[7]	Logic4 Sat-learning	Turing Award: Model Checking
25/26/27	10/29 10/31 11/03	First Order Logic: Syntax and semantics. Knowledge engineering in FOL. First Order Logic: Inference	[8] [9]	Logic5 Logic6
28/29/30	11/05 11/07 11/10	Intro to machine learning Decision Trees	[18.1; 18.2] [18.3]	Intro-Learning DT1	ML Glossary Weka (Collection of machine learning algorithms)
31/32	11/12 11/14	Computational Learning Theory PAC Learning Decision Lists	[18.5]	PAC
33	11/17	Noise and Overfitting	[18.3]	DT2
34	11/19	Introduction to Neural Networks	[20.5]	Intro-ANN	IBM plans "brain-like" computers
35	11/24	Neural Network Concepts Perceptron Learning	[20.5]	ANN1 ANN2
36	11/26	Expressiveness of Perceptrons	[20.5]	ANN3
37	12/1	Multi-Layer Networks Backpropagation K- Nearest Neighbor	[20.5] [20.4]	ANN4 K-NN
38	12/3	Support Vector Machines Ensemble Learning Exam Info	[20.6] [18.4]	SVM Ensemble Learning Exam Info	SVM Light Netflix Prize