6.892 Statistical Computer Vision and Learning Theory
or, how to train your brain.
Summary
When most creatures are born they cannot see. Between birth and adulthood
some set of changes occur that enable vision. Ever wonder what an infant's
brain might be learning as observes the world? Is there a simple theory
that might explain the computations that underlie all of vision?
This course will cover a branch of computer vision and learning that
is based on probabilistic analysis. Our primary focus will be on images,
but we will be covering a number of more general subjects such as Bayesian
decision theory, unsupervised learning, simulated annealing, and markov
random fields (hidden and not hidden). In vision we will cover theories
of unsupervised learning such as the Helmholtz(TM) machine, and Kohonen
nets. We will ask and even try to answer the question, "What are edges
for?" We will talk about supervised learning techniques such as neural
networks, and radial basis functions
Some theoretical knowledge of linear algebra and probability is a must.
Interest in the brain a benefit. Some basic knowledge of computer vision
and graphics will be assumed.
Logistics
- Instructor
- Professor Paul
A. Viola
- viola@ai.mit.edu (preferred point of contact)
- Room NE43-733 Phone x3-8828
- MIT AI Lab
- Syllabus
- Syllabus
- Bibliograpy
- Check out the new bibliography.
- Time
- Tuesday and Thursday 1:00 to 2:30 PM
- Location
- 36-372
- Format
Class will consist both of lectures and guided discussion of papers.
Students are strongly encouraged both to read and form opinions about the
papers presented. Discussion based on theoretical or empirical analysis
is especially encouraged.
- Lectures (Hey it works now!!)
Grading
There is one required project or paper which must be presented in class.
A number of Matlab based example labs will be made available to the
class. Solutions to these will not be graded but can form the basis of
a class project.
Text
- A collection of papers from the literature. Sections from
- Duda, R.O. and P.E. Hart, Pattern Classification and Scene Analysis
- Marr, D. Vision
- Cover and Thomas, Elements of Information Theory
- Haykin, Neural Networks
- and other texts.
Recommeded Prerequisites
- Some combination of (or equivalent):
- 6.034 (Artificial Intelligence)
- 18.06 (Linear Algebra)
- 6.041/18.313 (Probabitly)