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Machine Learning

10-701/15-781, Fall 2006


Eric Xing and Tom Mitchell
School of Computer Science, Carnegie-Mellon University


Grading

The requirements of this course consist of participating in lectures, midterm and final exams, 4 problem sets and a project. This is a PhD level class, and the most important thing for us is that by the end of this class students understand the basic methodologies in machine learning, and be able to use them to solve real problems of modest complexity. The grading breakdown is the following:

Exams:

Homework resources and collaboration policy

Homeworks and exams may contain material that has been covered by papers and webpages. Since this is a graduate class, we expect students to want to learn and not google for answers.

Homeworks will be done individually: each student must hand in their own answers. It is acceptable, however, for students to collaborate in figuring out answers and helping each other solve the problems. We will be assuming that, as participants in a graduate course, you will be taking the responsibility to make sure you personally understand the solution to any work arising from such collaboration. You also must indicate on each homework with whom you collaborated.

The final project may be completed by small teams.

Late homework policy

Homework regrades policy

If you feel that we have made an error in grading your homework, please turn in your homework with a written explanation to  Sharon Cavlovich. and we will consider your request. Please note that regrading of a homework may cause your grade to go up or down.

Homework assignments

We will anticipate 4 problem sets during the semester, in addition to a final project. Problem sets will consist of both theoretical and programming problems.  

Final project

Your class project is an opportunity for you to explore an interesting machine learning problem of your choice in the context of a real-world data set. Projects will either try to extend one the methods discussed in class, apply a method to a new dataset or apply a new / revised algorithm to one of the problems discussed in class (for example, a new clustering or classification algorithm). Projects can be done by you as an individual, or in teams of two students.  Instructors and TAs will consult with you on your ideas, but of course the final responsibility to define and execute an interesting piece of work is yours. Your project will be worth 20% of your final class grade, and will have 4 deliverables:

  1. Proposal:1 page (10%) due Oct 24
  2. Midway Report:3-4 pages (20%) due Nov 9
  3. Final Report: 8 pages (40%) due Nov 29
  4. Poster Presentation: (30%) due Nov 30 


See Project Guidelines and Project Suggestions for more details.

Note to people outside CMU

Please feel free to reuse any of these course materials that you find of use in your own courses.  We ask that you retain any copyright notices, and include written notice indicating the source of any materials you use.