6.891 Machine Learning and Neural Networks
News
12/10 
Last year's final...
here.

10/29 
Solutions to the midterm in pages:
soln.ps.gz,soln.pdf.gz

10/7 
The final will be on 12/13, at 1:304:30pm, in 3270.

Summary
We will cover progress in machine learning and neural networks
starting from perceptrons and continuing to recent work in "bayes
nets" and "support vector machines". We will explore basic
algorithms, including backpropagation, Boltzmann machines, mixtures of
experts, and hidden Markov models. Throughout the relationship to
statistical inference will be emphasized. Students will find that
having had either 6.034 or 6.011 will be extremely helpful
(i.e. familiarity with decision making and estimation in the presence
of uncertainty and noise).
This is very likely to satisfy the AI graduate requirement for Area II
graduate students in EECS.
This class is considered an elective in the Artificial Intelligence
concentration required of EECS undergraduates.
Prerequisites
6.041/6.042/18.313 (Probability)
One of the following is recommeded:
 6.034 (Artificial Intelligence)
 6.011 (Intro to Communications, Control and Signal Processing)
Information about Matlab can be found here.
. Some of you may
have taken 6.003 (which uses Matlab). They also maintain a web page
describing
MATLAB.
Instructor
Professor Paul
A. Viola
viola@ai.mit.edu (preferred point of contact)
Room NE43773 Phone x38828
MIT AI Lab
Teaching Assistant
Kinh H. Tieu
tieu@ai.mit.edu (preferred point of contact)
Room NE43771 Phone x37547
MIT AI Lab
Office Hours: Wednesday 34 PM and Friday 45 PM
Lectures
Time: Wednesday and Friday 11 AM12:30 PM
 Note: There will be two Monday Lectures: 9/20 and 11/29 (location to be announced).
Location: 3442
 I may try to move this in the first week of class...
Recitations
There are two sections each week (of the same material,
you need only attend one of them):
 Tuesday, 121 PM (34302)
and 45 PM (34301)
Review Section
Monday 34 PM (NE43777)
Format
Class will consist of lectures and discussion of material coming
from the textbook and related research papers. Discussion is
strongly encouraged so it will help to have read ahead of
lectures.
Grading
There will be a midterm and a final. Each will cover roughly one half of the course material.
Six problem sets will be assigned. Each will have exercises and
programming assignments in Matlab.
There will be a final project. This is your chance to work on a
machine learning project of your choosing. The scope of the project
is roughly 2 problem sets.
 Lecture Schedule
Schedule: The schedule of lectures is not yet ready.
 Lectures Slides
The lectures slides have been placed in
this directory.
 Problem Sets
The policy on selfgrading is available...
Pointers to related and
clarifying information.
 Text
Duda, Hart and Stork, Pattern Classification
A collection of papers from the literature.