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
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.
The course grade will be computed as follows:
Assignments and class participation: 10%
Midterm exam: 33%
Final exam 33%
Mid-term exam: Wednesday 16 March at 4:30PM
Final exam: Wednesday 4 May at 4:30PM
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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
The University Catalog, http://catalog.gmu.edu, is the central resource for university policies affecting student, faculty, and staff conduct in university affairs.
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.