Mailing List

For questions email cs4700ta-l "at" lists.cs.cornell.edu. (Note: Remove the extra spaces).  The list is set to mail all the TA's and Prof. Lipson -- 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.

Reference Material

Lab accounts

Accounts at the Computer Science Undergraduate Lab (CSUG) should already be set up for all pre-enrolled students over the summer, and for newly registered students by the end of the add-drop period. If you do not have an account but need one sooner, please submit a request. You must authenticate your account before you can start using it. This will also grant you card access to the lab and to the MSDNAA software library for downloading software.

Accounts on CMS should also be set up for all pre-enrolled students. If you do not have an account, please contact the head TA and ask to be added.

Prerequisites

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.

If you have not taken prerequisites but want to take this course anyway, please be aware that there is a significant programming component. Get a copy of Introduction to Algorithms (Cormen et al.) and read and understand the chapters on Elementary Data Structures, Hash tables, Binary Search trees, and Elementary Graph algorithms. Do a few of the exercises to test you knowledge You must be comfortable programming in some high level language.

Syllabus

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, and local search.
• Constraint satisfaction problems: definition, search and inference, and study of structure.
• Adversarial search: games, optimal strategies, imperfect, real-time decisions.
• Logical agents: propositional and first order logic, knowledge bases and inference.
• Uncertainty and probabilistic reasoning: probability concepts, Bayesian networks, probabilistic reasoning over time, and decision making.
• Learning: inductive learning, concept formation, decision tree learning, statistical approaches, neural networks

Note: some of the topics listed above may be covered only briefly, depending on time. Detailed reading information will be provided in the lectures notes and homework assignments.

Grading This is a 3-credit course. Grades will be determined based on a midterm, a final exam, homework assignments, and class participation.

• 15%: Midterm
• 35%: Final Exam
• 45%: Homework (~6 assignments)
• 5%: Participation

The midterm is an in-class examination (Friday, Oct 9) and the final will be given on the day and time slot scheduled by the university. 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.

Re-grading: Although you are encouraged to talk with any TA about the questions, only the head TAs may award back points (or detract points if they  find new things wrong) if you want a re-grade.

Q-Exam grad students: If you are a graduate student needing a Qualifier Exam, you need to take only the final. However, you can the midterm too if you want to "spread the risk".

Tentative Schedule