6.867 Machine Learning (Fall 2008)

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Syllabus

Lectures

Recitations

Projects

Problem sets

Exams

References

Matlab 		Lectures:
  • Prof. Tommi Jaakkola (tommi at csail dot mit dot edu)
  • Prof. Michael Collins (mcollins at csail dot mit dot edu)
Lecture times: Mon/Wed 1-2:30pm in 54-100

Recitations/tutorials:

  • David Sontag (dsontag at mit dot edu), office hours: Tue 1-2pm, 32-G4 lounge
  • Hung-An Chang (hungan_c at mit dot edu), office hours: Thr 4-5pm, 32-G4 lounge
  • Zoran Dzunic (zoki at mit dot edu), office hours: Thr 3-4pm, 32-G4 lounge
Recitation times (pick one): F10 (26-210) or F11 (26-210) or F2 ( 26-328) or F3 (26-328). The first recitation will be Friday, Sep 12.

Problem sets:

There will be a total of 5 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 solutions. Copying will not be tolerated. If you collaborate, you must indicate all of your collaborators.

Exams:

  • Midterm, in class, October 15 (Wed)
  • Final exam, in class, December 8 (Mon)

    • Project:

  • Electronic submission (in pdf format), December 4 (Thu).
    • 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 (a two person group will have 6 pages, a three person group will have 8 pages, and so on). 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 Thursday, December 4. Electronic submission is required but we can accept only postscript or pdf documents.

    The projects can be literature reviews, theoretical derivations or analyses, applications of machine learning methods to problems you are interested in, or something else (to be discussed with course staff).

    Grading:

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

    Text:

    There are a number of useful texts for this course but each covers only some part of the class material.

    • Bishop, "Pattern Recognition and Machine Learning", 2007
    • Duda, Hart, Stork, "Pattern Classification", 2000
    • Hastie, Tibshirani and Friedman, "Elements of Statistical Learning: Data Mining, Inference and Prediction", 2001
    • MacKay, "Information Theory, Inference, and Learning Algorithms", 2003.
      Available on-line here
    • Mitchell, "Machine Learning", 1997.

    You are responsible for the material covered in lectures (most of which will appear in lecture notes in some form), problem sets, as well as material specifically made available and indicated for this purpose. The weekly recitations/tutorials will be helpful in understanding the material and solving the homework problems.