EECS 545: Machine Learning

University of Michigan, Fall 2014

Instructor: Clayton Scott (clayscot)
Classroom: GG Brown 1504
Time: MW 10:30--12:00
Office: 4433 EECS
Office hours: Mon. 1-4 PM or by appointment
GSI: Takanori Watanabe (takanori)
GSI office hours: Tues. 2-5 PM in EECS 2420

Required text: None.

Recommended texts:

Additional references:

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.

The topics covered will be similar to those from last year.


Homework: 45%
Midterm exam: 30%, Thursday Nov. 20, 6-9 PM, location TBA.
Final project: 25%. Project due Friday Dec. 12, reviews due Wed. Dec. 17 at 12 noon.

Homeworks will be assigned weekly. Applications will be developed through Matlab programming exercises, including face recognition, spam filtering, handwritten digit recognition, image compression, and image segmentation. Most assignments will involve some computer programming. MATLAB will serve as the official programming language of the course. I will sometimes provide you with data, fragments of code, or suggested commands, in MATLAB.

Exam: Mid November, exact date TBA.
You may use three cheat sheets (front and back), and no other materials are allowed. Please notify me the first week of class if you have a conflict.

Final Project:
There will be a final project. Groups will be allowed. The project must explore a methodology or application not covered in the lectures. You will be asked to select a paper on a methodology not covered in class, and implement the method. Because of the expected large enrollment, students will assist in grading by reviewing and evaluating other projects in the class.

Collaboration on homeworks:
Each student will prepare the final write-up of his or her homework solutions without reference to any other person or source, aside from the student's own notes or scrap work. Students may consult classmates for the purpose of brainstorming, but not for obtaining the details of solutions. Under no circumstances may you copy solutions or code from a classmate or other source.

Computer use in class:
You may use your computer in class for note taking or note viewing, but otherwise please refrain from using computers or personal electronic devices during class, as these are distracting to me and your classmates.

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.