Foundations of Artificial Intelligence

CS472 - Fall 2007
Cornell University
Department of Computer Science

Time and Place
First lecture: Friday, August 24, 2007
Last lecture: Friday, November 30, 2007
  • Monday, 11:15am - 12:05pm in PH219
  • Wednesday, 11:15am - 12:05pm in PH219
  • Friday, 11:15am - 12:05pm in PH219

First Prelim Exam: Friday, September 28.
Second Prelim Exam: Monday, November 19.
Final Exam: Monday, December 10, 9:00am - 11:30am, Location OH255

Thorsten Joachims [], 4153 Upson Hall.
Teaching Assistants
Thomas Finley
Alexander Chao
Ilya Sukhar
Griffin Dorman
Rick Keilty
Brian Rudo
Mailing List and Wiki

[] We'd like you to contact us by using this mailing list.  The list is set to mail all the TA's and Prof. Joachims -- you will get the best response time by using this facility, and all the TA's will know the question you asked and the answers you receive.
[CS472 Wiki] This is our Wiki. If you cannot view it, let us know. We may post important things here. Feel free to post important things here as well, though obviously without dishonoring Cornell's academic principles.

Office Hours
Monday1:30pm - 2:30pm Rick Keilty CSUG Lab
Monday2:30pm - 3:30pm Alexander Hong Chao 328 Upson Hall
Tuesday3:30pm - 5:00pm Thomas Finley 5156 Upson Hall
Wednesday12:30pm - 1:30pm Griffin Dorman 328 Upson Hall
Wednesday1:30pm - 2:30pm Thorsten Joachims 4153 Upson Hall
Thursday2:30pm - 4:00pm Thomas Finley 5156 Upson Hall
Friday12:15pm - 1:15pm Ilya Sukhar 328 Upson Hall
This course introduces the theoretical and computational techniques that serve as a foundation for the study of artificial intelligence (AI). Topics to be covered include the following:
  • Introduction of AI and background: What is AI? Related fields
  • Problem solving by search: principles of search, uninformed (“blind”) search, informed (“heuristic”) search, genetic algorithms, game playing
  • Learning: inductive learning, concept formation, decision tree learning, statistical approaches, neural networks
  • Knowledge representation and reasoning: knowledge bases and inference; constraint satisfaction; planning; theorem-proving; Bayesian networks
  • Natural language understanding: syntactic processing, ambiguity resolution, text understanding
Reference Material
The main textbook for the class is:
              Artificial Intelligence: A Modern Approach, Russell and Norvig, Prentice-Hall, Inc., second edition.
This course has no prerequisites other than familiarity with basic data structures and programming (e.g., CS211) and the basic mathematical skills obtained in CS280. An understanding of inference in prepositional logic and basic blind search techniques (i.e., breadth-first and depth-first search) is also assumed, but background readings in these topics can be provided for those with a deficiency in this area.
  • 08/24: R&N Chapter 1
  • 08/27: R&N Chapter 2
  • 08/31: R&N Chapter 3
  • 09/03: R&N 4.1-4.2.
  • 09/07: R&N 4.3. and p. 120.
  • 09/12: R&N Chapter 5
  • 09/17: R&N Chapter 6
  • 09/21: R&N 17.1
  • 09/24: R&N 17.2-17.3
  • 09/26: R&N 21.1-21.4
  • 10/03: R&N 18.1-18.2, 20.4
  • 10/10: R&N 18.3
  • 10/17: R&N 18.5
  • 10/22: R&N 20.5 (single layer)
  • 10/24: Schoelkopf & Smola 7.1-7.3, 7.5 (online)
  • 10/26: R&N 20.5 (remainder)
  • 10/31: R&N 20.6-20.7, Schoelkopf & Smola 7.4 (online)
  • 11/02: Optimizing Search Engines Using Clickthrough Data (online)
  • 11/07: R&N 7.1-7.5
  • 11/09: R&N 7.6
  • 11/12: R&N Chapter 8
  • 11/14: R&N 9.1-9.3, 9.5
  • 11/21: R&N 10.3 (up to page 334)
  • 11/26: R&N 11.1-11.2
  • 11/28: R&N 11.3
Slides and Handouts
  • 08/24: Introduction (PDF)
  • 08/27: Agents and Problem-Solving as Search (PDF)
  • 09/03: Informed Search (PDF)
  • 09/07: Local Search (PDF)
  • 09/12: Constraint Satisfaction (PDF)
  • 09/17: Adversarial Search and Games (PDF)
  • 09/21: Markov Decision Processes and Reinforcement Learning (PDF)
  • 10/03: Instance Based Learning (PDF)
  • 10/10: Decision Trees (PDF)
  • 10/15: Overfitting and Cross-Validation (PDF)
  • 10/17: Statistical Learning Theory (PDF)
  • 10/22: Perceptron and Optimal Hyperplanes (PDF)
  • 10/26: Neural Nets (PDF)
  • 10/31: Support Vector Machines and Kernels (PDF)
  • 11/02: Learning Rankings for Search Engines (PDF)
  • 11/07: Knowledge-Based Systems (PDF)
  • 11/12: First-Order Logic and Resolution (PDF)
  • 11/21: Planning (PDF)
Homework Assignments

Homework assignments are available via CMS. Assignments are due in hardcopy. Graded homework assignments and the prelims can be picked-up in Upson 360. The opening hours are Monday-Friday, 10am-12pm and 2pm-4pm (check: are these still the current times?).

See critique guidelines and samples on the Wiki.

Grading CS472
This is a 3-credit course. Grades will be determined based on two written exams, a final exam, homework assignments, and class participation.
  • 25%: 2 Prelim Exams
  • 25%: Final Exam
  • 45%: Homework (~6 assignments)
  • 5%: Participation

All assignments are due at the beginning of class on the due date. Assignments turned in late will drop 5 points for each period of 24 hours for which the assignment is late. In addition, no assignments will be accepted after the solutions have been made available.

Roughly: A=92-100; B=82-88; C=72-78; D=62-68; F= below 60

Although you are encouraged to talk with any TA about the questions, only Tom the TA may award back points (or detract points if his eagle eyes find new things wrong) if you want a regrade.

You may take CS473 "Practicum in Artificial Intelligence" as a supplement to CS472.  CS472 is a co-requisite for CS473.
Academic Integrity
This course follows the Cornell University Code of Academic Integrity. Each student in this course is expected to abide by the Cornell University Code of Academic Integrity. Any work submitted by a student in this course for academic credit will be the student's own work. Violations of the rules (e.g. cheating, copying, non-approved collaborations) will not be tolerated.