6.034 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 partial derivatives and dot products. If you have not taken 18.02 (or are not taking it concurrently), you should really wait to take 6.034 until you have.
The course covers the following areas:
- Machine learning (4 lectures)
- Search (2 lectures)
- Knowledge representation and Inference (3 lectures)
- Uncertainty (3 lectures)
- Planning (2 lectures)
- Games
- Natural Language Processing
- Guest lectures on:
- Cognitive Science
- Medical Infomatics
- Robotics
- Vision
- 2 x 1.5 hr classes (MW11-12:30, 32-123)
- 1 recitation with TA (on Fridays - to be scheduled)
- On-line 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 Projects
- 2 in-class Quizzes
- Final
- 30% Final
- 30% Quizzes
- 25% Projects
- 15% On-line assignments + Recitation Participation
- You must complete both projects to pass the course.
- The on-line assignments 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%. A minimum average score of 75% on the on-line
assignments is required to pass the course.
- You have a total of 3 grace days for late submission of problem sets
and projects.
The 6.034 collaboration policy is identical to the 6.001 policy, which is
explained in more detail here. The
highlights are listed below, but we expect you to have read the detailed policy.
- Everything you hand in for credit in this subject is supposed to be your
own work; this includes on-line work.
- The on-line problems should be done individually, possibly with assistance
from TAs.
- We encourage you to work with one or two people to figure out approaches to
the projects and to get help in debugging, but everything you hand in, including
code, must be your own work. You must identify the people you worked with on
your project.
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