6.867 Machine Learning

6.867 Machine Learning

Fall 2001

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.

Lectures:

Prof. Tommi Jaakkola, tommi@ai.mit.edu

Tue/Thu 2:30-4pm in E37-212, first lecture is on September 11

Recitations:

Nathan Srebro, nati@mit.edu

Wednesdays 1-2:30, 36-144
OR:
Friday 11-12:30, 34-302

Problem sets:

There will be a total of 4 or 5 problem sets and they are due about a week and a half form when they are handed out. 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 version. Copying will not be tolerated. If you collaborate, you will have to indicate all of your collaborators.

The problem sets will be graded by a rotating group of students (yes, by yourselves) with the guidance of your TA. Each problem set will be graded in a single grading session, usually on the Monday after it is due, starting at 5pm. Every student is required to participate in one grading session. Students should sign up for grading, using the electronic sign-up sheet, before the due date of the first problem set.

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 for grading

Due dates for the problem sets are indicated in the course calendar.

Exams:

  • Midterm, in class, October 25
  • Final exam, in class, December 11.

    • Project:

    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 December 6.

    We will provide you with more detailed suggestions and guidelines for the projects but they can be literature reviews, theoretical derivations or analyses, applications of machine learning methods to problems you are interested in, or something else.

    Grading:

    Your overall grade will be determined roughly speaking as follows: Midterm 15%, Problem sets 30%, Final 30%, Project 25%

    Text:

    The textbook for the course is M. Jordan and C. Bishop, Introduction to Graphical Models (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.

    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, recitations, problem sets, as well as in the chapters/sections of the text specifically indicated.