University of Michigan, Winter 2014
Instructor: Clayton Scott (clayscot)
Classroom: EECS 1003
Time: MW 9-10:30
Office: 4433 EECS
Office hours: Monday 1-2 or by appt.
Prerequisites:
Recommended books (on reserve at Engineering library):
Surveys and tutorials:
Lecture notes
Grading
Homework (40%)
Final report (40%)
Participation (20%)
Homework
Homework will be assigned progressively in lecture,
and
will be due at regular intervals. You will be given at least one week
advanced notice of the due date, but I recommend solving the problems as
they are assigned, since it will help with your understanding of the
lectures.
Final report
Each student will choose one or a few research
papers
on a particular topic in statistical learning theory, and write a report
that summarizes the contributions of the paper(s), including at least a
sketch of the main technical ideas. Reports will be evaluated by your
peers in a manner that mimics a conference review process. I may be out of
town the last week of classes, so the reports may be due slightly before
then, such as the last Friday before classes end.
Participation
Attendance, classroom interaction, and scribing
lecture notes.
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.