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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:
- Homework (4 assignments, 30%)
- Midterm (25%)
- Final exam (25%)
- Final project (20%)
Exams:
- The midterm and final exams will be open
book and open notes. Computers will not be allowed.
- Final exam date: Friday, December 15, 2006, 5:30-8:30pm
- Rescheduling of exams: It is impossible for us to accommodate individual requests to reschedule the exams. It is your responsibility to assure that you are in town and available for the final exam. Please take this into account as you plan flights for the winter holiday
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
- You will be allowed 2 total late days
without penalty for the entire semester. You may be late by 1 day
on two different homeworks or late by 2
days on one homework. Once those days are
used, you will be penalized according to the following policy:
- Homework is worth full credit at the beginning of class on the due date.
- It is worth half credit for the next 48 hours.
- It is worth zero credit after that.
- You must turn in at least n-1 of the n homeworks, even if for zero credit, in order to pass the course.
- Turn in all late homework assignments to Sharon Cavlovich.
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.
- HW1: Out Sept 14, due Sept 26 at the beginning
of class (handout, code)
HW1 Solution: Problem 1,2,4, Problem 2.5, Problem 3 (courtesy of Lillian Chang) - HW2: Out Sept 26, due Oct 5 at the beginning of
class (handout, code
for problem 4, data, clarifications)
HW2 Solution: writeup, code - HW3: Out Oct 5, due Oct 17 at the beginning of
class (handout, code
and data for problem 2, dataset graph for
problem 1)
HW3 Solution: writeup, Code for Problem 2, A Tighter Bound for Problem 3.1 (courtesy of Aaron Roth) - HW4: Part a out Oct 26, due Nov 7 at the
beginning of class (handout, data for problem 2)
Part b out Nov 9, due Nov 21 at the beginning of class (handout)
HW4 Solution: Problem 1, 2, Problem 4 (courtesy of Lillian Chang)
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:
- Proposal:1
page (10%) due Oct 24
- Midway
Report:3-4 pages (20%) due Nov 9
- Final Report: 8
pages (40%) due Nov 29
- 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.