EECS 545: Machine Learning

University of Michigan, Fall 2011

Instructor: Clayton Scott
Classroom: Dow 1010
Time: MW 10:30--12:00
Office: 4433 EECS
Email: clayscot
Office hours: Mon. 2-4
GSI: Takanori Watanabe (takanori)
GSI office hours: Tue. 3-5, 2420 EECS

Final Projects from Fall 2011

Final Projects from Fall 2009

Final Projects from Fall 2007

Required text: None. I will share my lecture notes prior to each lecture.

Primary recommended text:

Other recommended texts:

Additional references

Machine learning bibliography

Prerequisites: (the current formal prerequisite is currently listed as EECS 492, Artificial Intelligence, but this is inaccurate)

It is expected that students will have a good working knowledge of these topics. Students with most but not all of this background should be able to catch up during the semester with some additional effort.


These are projected topics for 2011. I can also lecture on new topics depending on students' interest. Applications will be developed through Matlab programming exercises.

Supervised Learning Unsupervised Learning Reinforcement Learning Additional Topics (post-midterm exam)

Homework: 30%
Midterm exam: 30%, Thursday November 10, 6-9 PM, location TBA.
Final project: 40%

About four homeworks will be assigned before the midterm. After the midterm you will be 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. 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. I will help you find groups as needed.

Make-up class:
I expect to be attending a conference on the last day of classes, Dec. 13. Therefore I am scheduling a make-up class for Thursday, September 15, 6:00 - 7:30 PM, location TBD.

Exam: Thursday November 10, 6-9 PM.
Collaboration of any form will not be allowed. Allowed materials will be specified in advance of the exam. Notify me this week if you have a conflict.

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.

Each group will turn in one product representative of the group. Solutions to homework problems obtained from outside 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.

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.