EECS 545: Machine Learning.

University of Michigan, Fall 2009

Instructor: Clayton Scott
Classroom: 1690 CSE
Time: MW 10:30-12
Office: 4433 EECS
Email:
Office hours: TBA

Final Projects from Fall 2007

Required text: None.

Recommended texts

Duda, Hart, and Stork, Pattern Classification, Wiley, 2001
Hastie, Tibshirani, and Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, 2001
Bishop, Pattern Recognition and Machine Learning, Springer, 2006

Additional references

Scholkopf and Smola, Learning with Kernels, MIT Press, 2002
Mardia, Kent, and Bibby, Multivariate Analysis, Academic Press, 1979.
Rasmussen and Williams, Gaussian Processes for Machine Learning, MIT Press, 2006.
Boyd and Vandenberghe, Convex Optimization, Cambridge University Press, 2004
MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge University Press, 2003

Machine learning bibliography

Prerequisites:

Probability: random variables, densities and mass functions, expectation, independence, conditional distributions, Bayes rule, maximum likelihood estimation, the multivariate normal distribution
Linear algebra: rank, nullity, linear independence, inner products, orthogonality, eigenvalue decompositions, least squares, pseudo-inverses, projections.

The current formal prerequisite is currently listed as EECS 492, Artificial Intelligence, but this is inaccurate.

Lecture notes:

Grading:
Homework: 35%
Exam: 10%
Participation: 5%
Final project: 50%

Homeworks:
Homework will be assigned every one or two weeks. The assignments will be smaller toward the end of the course, when you are working on your project.

Computer programming
Most or all assignments will involve some computer programming. MATLAB will serve as the official programming language of the course. You are free to use another language, such as R, but I will sometimes provide you with fragments of code, or suggested commands, in MATLAB.

Group work:
Group work will take place on two levels. You will work on homeworks in small groups of 2, and the final project in large groups of 3 or 4.

Exam: Time and location TBD.
Collaboration of any form will not be allowed. Allowed materials will be specified in advance of the exam.

Final Project:
There will be a final, open-ended group project. The project must explore a methodology or application (and preferably both) not covered in the lectures. The work must not simply reproduce the results of a paper, but explore some new aspect of a problem. I will assist groups in selecting a topic as much as necessary. The project will be judged based on clarity, thoroughness, and originality. The project will be graded based on the following components:

Project proposal (10%): Due date TBA. 2 pages max. Each group must meet with me in advance of the proposal deadline to discuss the project.
Progress report (20%): Due date TBA. 5 pages max.
Final report (40%): Due date TBA. 10 pages max.
Poster presentation (30%): Date, location TBD

All written documents must be single spaced, 12 point font, with at least one inch margins. Page counts do not include figures, tables, and references. Figure captions should be self-contained.

Collaboration:
Each group will turn in one product representative of the group. Group members may receive unequal grades if there is a significant descrepancy in their effort. Solutions to homework problems found in other sources may not be used.

Honor Code
All undergraduate and graduate students are expected to abide by the College of Engineering Honor Code as stated in the Student Handbook and the Honor Code Pamphlet. This applies to all aspects of the course.

Students with Disabilities
Any student with a documented disability needing academic adjustments or accommodations is requested to speak with me during the first two weeks of class. All discussions will remain confidential.