George Mason University
Volgenau School of Engineering
Department of Computer Science

CS 782 Machine Learning

Meeting time: Wednesday 4:30 pm – 7:10 pm

Meeting location: Art and Design Building 2003

Instructor: Dr. Gheorghe Tecuci, Professor of Computer Science

Office hours: Wednesday 7:20 pm – 8:10 pm
Office: Nguyen Engineering Building 4613
Phone: 703 993 1722
E-mail: tecuci at gmu dot edu

Course Description

Machine Learning is concerned with the development of computer systems that are able to improve their performance at some task by learning from input data, from their own problem solving experience, and/or from a user. It studies all sorts of learning algorithms (symbolic, probabilistic, neural), and a wide variety of learning tasks. This course presents the principles, strategies, major methods, systems, applications, open issues, and research directions in Machine Learning. Covered topics include: Decision trees learning, Rule induction (Learning rule sets, Inductive logic programming), Instance-based approaches (k-NN, Locally weighted regression, Collaborative filtering, Case-based, Analogy), Bayesian learning (Naïve Bayes, Bayesian networks, EM), Neural networks and deep learning, Model ensembles (Bagging, Boosting, ECOC, Staking), Support vector machines, Reinforcement learning, Multistrategy learning and learning assistants, and Learning theory. The course will include experimentation with learning systems implementing the methods discussed in class. It will also include a project involving significant outside study and preparation of a presentation and demo to the class.

Detailed lecture notes with required and recommended readings will be posted before each class meeting. Several recommended books are on 2-hour reserve in Johnson Center.

This course will use Blackboard (see http://gmu.blackboard.com) to post lecture notes, papers, assignments, and grades. The students will also submit their assignments through Blackboard. Students have accounts on Blackboard and can download the posted documents by going to courses.gmu.edu and logging in using their Mason ID and passwords.

Grading Policy

The course grade will be computed as follows:

Assignments and class participation: 10%

Project: 24%

Midterm exam: 33%

Final exam 33%

Exam Dates

Mid-term exam: Wednesday 16 March at 4:30PM

Final exam: Wednesday 4 May at 4:30PM

Email Communication

Email to tecuci@gmu.edu and start the subject of the message with CS782.

Please try to limit the size of the files you are emailing.

GMU Email Accounts

Students must activate their GMU email accounts to receive important University information, including messages related to this class.

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 (703) 993-2474. All academic accommodations must be arranged through the ODS. http://ods.gmu.edu.

Other Useful Campus Resources

Writing Center: A114 Robinson Hall; 703 993 1200; http://writingcenter.gmu.edu

University Libraries “Ask a Librarian” http://library.gmu.edu/mudge/IM/IMRef.html

Counseling And Psychological Services (CAPS): 703 993 2380; http://caps.gmu.edu

University Policies

The University Catalog, http://catalog.gmu.edu, is the central resource for university policies affecting student, faculty, and staff conduct in university affairs.

Honor Code

Mason is an Honor Code university. You are expected to abide by the GMU honor code.

Information on the university honor code can be found at http://academicintegrity.gmu.edu/honorcode/.

Additional departmental CS information: http://cs.gmu.edu/wiki/pmwiki.php/HonorCode/CSHonorCodePolicies

Any collaboration between students on assignments and exams is unacceptable. If it is determined that two exams have not been done independently, then F grades will be assigned for this course.