Hwk. #4 posted. See below. Official due date is Friday, Dec., 6. But, we will accept, without penalty, solutions on CMS till 11:59am (note: AM), Tuesday, Dev., 10. No submissions accepted after that.
Hwk. #3 posted. See below. Due Wedn., Nov. 20, 11am, CMS.
TA mailing list: CS4700-STAFF-L@list.cornell.edu
Note: Re: Hwk #2 solution on CMS. Please note that uniform cost search as given in the solutions to HW-2 differs slightly from the algorithm mentioned in the textbook. Duplicate nodes were allowed into the queue in the solutions whereas the textbook adds nodes to the queue only if either it is absent in the queue or it is present with a higher cost (in which case the previous copy of the node is replaced).
Hwk. #2 posted. See below. Due Monday, Oct. 21, 11am, CMS.
Hwk. #1 posted. See below. Due Monday, Sept. 30, 11am, CMS.
Midterm: Thursday, Oct. 24, 7:30pm – 9:30pm, Room: Uris Hall G01
Final: Friday, Dec. 13, 2013, 2:00pm --- 4:30pm (room TBA)
CS-4701 project class announcements see here
Office hours Ph.D. TAs
4:00pm-5:00pm Jiaqi Zhai --- Upson 360
5:15pm-6:15pm Vikram Rao --- Upson 4121
4:00pm-5:00pm Mevlana Gemici --- Upson 4104
Prof. Bart Selman <email@example.com (meetings by appointment)>
Mevlana Gemici <firstname.lastname@example.org>
Vikram Rao Sudarshan <email@example.com>
Jiaqi Zhai <firstname.lastname@example.org>
Philip Berard <email@example.com>
Michael Daggett <firstname.lastname@example.org>
Eric Gold <email@example.com>
Chenxia Wu <firstname.lastname@example.org> [but grad]
Chris Yu <email@example.com>
Jane Yu <firstname.lastname@example.org>
Introduction (part 1) (pdf)
Introduction, cont. (part 2) (pdf)
Intelligent Agents (pdf)
Problem Solving and Search (part 1) (pdf)
Problem Solving and Search (part 2) (pdf)
Local Search (pdf)
Adversarial Search (part 1) (pdf)
Adversarial Search (part 2) (pdf)
Constraint Satisfaction (updated!) (pdf)
Knowledge Representation and Reasoning (Part 1) (pdf)
Knowledge Representation and Reasoning (Part 2) (pdf)
Intro Machine Learning (pdf)
Decision Tree Learning (pdf)
PAC Learning (pdf)
Neural Networks (pdf)