Spring 2004

6.034 ARTIFICIAL INTELLIGENCE introduces representations, techniques, and architectures used to build applied systems and to account for intelligence from a computational point of view. Applications of rule chaining, heuristic search, logic, constraint propagation, constrained search, and other problem-solving paradigms. Applications of decision trees, neural nets, SVMs and other learning paradigms.

PREREQUISITES

• 6.001
  We will have regular assignments that expect you to be able to read and write Scheme. This is the only formal pre-requisite.
  
  
• 18.02
  We will assume that you know what the chain rule is and what a dot product is, and a partial derivative, etc. If you have not taken this, you should really wait to take the subject until you have.
  
  

The course covers three major areas:
•   Search (2 weeks)
  • Graph search
  • Constraint satisfaction
  • Games
•   Knowledge representation and Inference (5 weeks)
  • Propositional and First Order Logic
  • Rule-based systems
  • Natural Language
•   Machine learning (5 weeks)
  
  • Nearest Neighbors
  • Decision Trees
  • Neural Networks
  • SVM

• 2 x 1.5 hr classes (MW11-12:30, 66-110)
• 1 recitation with TA (on Fridays - to be scheduled)
• On-line text + exercises
  •   Recommended book (available at Quantum & Amazon)
    is Russell & Norvig, AI: A Modern Approach (2nd ed). This book is only for supplementary reading; all of the course material is covered in the notes.
• On-line problem sets
• 2 in-class quizzes (March 8, April 14)
• Final

• 30% Final
• 40% Quizzes
• 20% On-line assignments
  
• 10% Recitation Participation
  
• The on-line exercises and problems are an essential component of the subject and are required. A 90% score on any on-line assignment gets full credit. There is no difference between 90% and 100%. Scores below 75% will lead to a grade of Incomplete in the subject.
  
• Problems submitted late will receive half credit unless you have a valid reason and make an arrangement with your TA.

• Everything you do for credit in this subject is supposed to be your own work; this includes on-line work.
• You can talk to other students (and TAs) about approaches to problems, but then you should sit down and do the problem yourself. This is not only the ethical way but also the only effective way of learning the material.