Instructor: Prof. Harry
Wechsler wechsler@gmu.edu
Course Description –
Surveys
machine learning concerning development of intelligent adaptive systems that
are able to improve through learning from input data or from their own
problem-solving experience. Topics provide broad coverage of developments in
machine learning, including basic learning strategies and multi-strategy
learning.
Objectives and Outcomes – Theory and practice for model
selection and prediction using Regularization, Statistical Learning {structural
risk minimization, support vector machines, semi-supervised learning, and
transduction}, non-linear optimization, and Randomness and Complexity, for the
purpose to develop robust methods for classification, regression, clustering,
and dimensionality reduction.
Time,
Day, and Venue:
W – Wednesday, 4:30 pm – 7: 10 pm, Science and Technology I 206
Office
Hours: Wednesday,
3:15 – 4:15 pm (ENGR - 4448)
http://registrar.gmu.edu/calendars/2012Spring.html
First day of classes: Wednesday, January 25
Spring break: no class on Wednesday, March 14
Midterm (“closed
books and closed notes”): Wednesday, March 21
Last day of classes: Wednesday, May 2
http://registrar.gmu.edu/calendars/2012SpringExam.html
Final
Exam: Wednesday, May 9, 4:30 pm – 7:15 pm
Grade
Composition: 100%
- Homework: 25%
- Midterm: 25%
- Term (team) Project: 25%
- FINAL: 25%
Textbook: Cherkassky and Mulier, Learning from Data
(2nd edition.), Wiley, 2007.
Topics:
- Textbook (Chaps. 1 – 10)
- Research and Survey Papers
Term (Team) Project ~ Presentation and Final Report ~ Wednesday, May 2
ACADEMIC
INTEGRITY
GMU is an Honor Code university; please see the University Catalog for a full description
of the code and the honor committee process. The principle of academic integrity
is taken very seriously and violations are treated gravely. What does academic integrity
mean in this course? Essentially this: when you are responsible for a task, you
will perform that task. When you rely on someone else’s work in an aspect of
the performance of that task, you will give full credit in the proper, accepted
form. Another aspect of academic integrity is the free play of ideas. Vigorous
discussion and debate are encouraged in this course, with the firm expectation
that all aspects of the class will be conducted with civility and respect for
differing ideas, perspectives, and traditions. When in doubt (of any kind)
please ask for guidance and clarification.
GMU EMAIL ACCOUNTS
Students must use their Mason email accounts—either the existing “MEMO” system
or a new “MASONLIVE” account to receive important University information,
including messages related to this class. See http://masonlive.gmu.edu
for more information. NOTE: Weekly email messages
are sent to the class
including among others reading
assignments, homework, and journal / conference papers.
OFFICE OF DISABILITY SERVICES
If you are a student with a disability and you need academic accommodations,
please see me and contact the Office of Disability Services (ODS) at 993-2474.
All academic accommodations must be arranged through the ODS. http://ods.gmu.edu