Week 1: 8/25

Lectures: Course overview, what is AI? [ slides ] intro to python [ slides ] and some [ code ]
Reading: Russell and Norvig, chapter 1.
Assignments: First programming assignment is available

Week 2: 9/1

Lectures: Agents [ slides ] Search [ slides ]
Reading: Russell and Norvig, chapters 2,3.
Python tutorial: Wed 4pm, Thu 4pm [ Slides ], [ Examples ], [ Exercise ], [ Solution ]

Week 3: 9/8

Lectures: Uninformed search methods [ slides ]
Reading: Russell and Norvig, chapter 3.

Week 4: 9/15

Lectures: Informed search [ slides ]. Local search [ slides ].
Reading: Russell and Norvig, chapter 4.

Week 5: 9/22

Lectures: Global optimization [ slides ].
Reading: Russell and Norvig, chapter 4.

Week 6: 9/29

Lectures: Games [ slides ]. Constraint satisfaction problems [ slides ]
Reading: Russell and Norvig, chapters 5,6.
Assignments: Assignment 3 is available.

Week 7: 10/6

Lectures: Logical agents using propositional logic [ slides ] and more [ slides ].
Reading: Russell and Norvig, chapter 7.

Week 8: 10/13

Lectures: First order logic [ slides ].
Reading: Russell and Norvig, chapter 8.
Assignments: Assignment 4 is available.

Week 9: 10/20

Lectures: Midterm. Inference in first order logic [ slides ]
Reading: Russell and Norvig, chapter 9.
Assignments: Assignment 5 is available.

Week 10: 10/27

Lectures: Inference in first order logic, continued. Agents in an uncertain environment [ slides ]
Reading: Russell and Norvig, chapter 13.

Week 11: 11/3

Lectures: Bayesian networks [ slides ]
Reading: Russell and Norvig, chapter 14.

Week 12: 11/10

Lectures: Inference in Bayesian networks [ slides ]. Approximate inference and learning in Bayesian networks [ slides ]
Reading: Russell and Norvig, chapter 14.

Week 13: 11/17

Lectures: Learning from data [ slides ] (modified on thu)
Reading: Russell and Norvig, chapter 20.

Week 14: 12/1

Lectures: Statistical learning [ slides ] and decision trees [ slides ]
Reading: Russell and Norvig, chapter 20.

Week 15: 12/8

Lectures: Project presentations