6.867 Machine Learning
6.867 Machine Learning
This introductory course on machine learning will give an overview of
many techniques and algorithms in machine learning, beginning with
topics such as simple perceptrons and ending up with more recent
topics such as boosting, support vector machines, hidden Markov
models, and Bayesian networks. The course will give the student the
basic ideas and intuition behind modern machine learning methods as
well as a bit more formal understanding of how and why they work. The
underlying theme in the course is statistical inference as this
provides the foundation for most of the methods covered.
Here is a partial list of concepts, methods and tools dealt with in
Prof. Tommi Jaakkola, email@example.com
Tue/Thu 2:30-4pm in E37-212.
Greg Shakhnarovich firstname.lastname@example.org
Wed 11am-12pm in 38-166
Fri 3-4pm in 36-156
There will be a total of 6 problem sets, due roughly every two weeks.
The content of the
problem sets will vary from theoretical questions to more applied
problems. You are encouraged to collaborate with other students while
solving the problems but you will have to turn in your own
solution. Copying will not be tolerated. If you collaborate, you must indicate all of your collaborators.
Each problem set will be graded by a group of students
with the guidance of your TA. Each problem set will be graded
in a single grading session, usually on the first Monday after it is due,
starting at 5pm. Every student is required to participate in one grading
session. You should sign up for grading by contacting the TA, by
email or in person; doing it early increases the chances of getting
the preferred grading schedule. Students who do not register for
grading by the third week of the course, will be assigned to a problem
set by us.
If you drop the class after signing up for a grading session, please be
sure to let us know so we can keep track of students available for
grading. If you add the class durring the term, please remember to sign up
You are required to complete a class project. The choice of the topic
is up to you so long as it clearly pertains to the course material. To
ensure that you are on the right track, you will have to submit a one
paragraph description of your project a month before the project is
due. Similarly to problem sets, you are encouraged to collaborate on
the project. We expect a four page write-up about the project, which
should clearly and succintly describe the project goal, methods, and
your results. Each group should submit only one copy of the write-up
and include all the names of the group members. The projects will be
graded on the basis of your understanding of the overall course
material (not based on, e.g., how brilliantly your method works). The
scope of the projet is about 1-2 problem sets.
The projects are due on Wednesday, December 11th. The short proposal
should be turned in on or before October 29th.
The projects can be literature reviews, theoretical
derivations or analyses, applications of machine learning methods to
problems you are interested in, or something else.
Your overall grade will be determined roughly as follows:
Midterm 15%, Problem sets 30%, Final 25%, Project 30%
There are three useful texts for this course; each covers
some part of the class material, as well as things outside of the
scope of the class.
M. Jordan and C. Bishop, Introduction to Graphical Models. Draft version, accessible
electronically within MIT only. The book is at final stages of
writing and is not available in print. Please do not distribute
copies of this book. If you do, you will seriously endanger our
ability to use latest text books in the future.
Neural Networks for Pattern Recognition. Oxford University
R. O. Duda, P. E. Hart, D. G. Stork. Pattern
Classification . Wiley, 2000.
You will not be able to find all the course material in the text nor
do we plan to go through the chapters in order or in full. You are
responsible for the material covered in lectures and problem
sets, as well as in the chapters/sections of the text specifically
indicated. The weekly recitations/tutorials will be helpful in
understanding the material and solving the homework problems.