CS 782 – Spring 2012

Machine Learning – 16577 – CS 782 – 001

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)


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


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.




- Textbook (Chaps. 1 – 10)

- Research and Survey Papers


Term (Team) Project  ~ Presentation and Final Report  ~ Wednesday, May 2


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.

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.

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