CS539 - Fall 2015

CS539 Machine Learning

Syllabus— Fall 2015

WARNING: Small changes to this syllabus may be made during the semester.

COURSE DESCRIPTION:

Machine learning is concerned with the design and study of computer programs that are able to improve their own performance with experience, or in other words, computer programs that learn. In this graduate course we cover several theoretical and practical aspects of machine learning. We study different machine learning techniques/paradigms, including decision trees, neural networks, genetic algorithms, Bayesian learning, rule learning, and reinforcement learning. We discuss applications of these techniques to problems in data analysis, knowledge discovery and data mining.

For the catalog description of this course see the WPI Graduate Catalog.

CLASS MEETING:

Time: Tuesdays and Fridays 3:00-4:20 pm
Room: HL202

GRADES:

3 Homework Assignments
2 Quizzes (15% each):	30%
Final Exam:	25%
2 Projects (20% each):	40%
2 Project Presentations (1.5% each):	3%
Class Participation:	2%

Your final grade will reflect your own work and achievements during the course. Any type of cheating will be penalized with an F grade for the course and will be reported to the WPI Judicial Board in accordance with the Academic Honesty Policy.

Note that this course follows the guidelines established by the WPI faculty in May 2010:

"A student is expected to expend at least 56 hours of total effort for each graduate credit. This means that a student in a 3-graduate credit 14-week course is expected to expend at least 12 hours of total effort per week."

Hence, please expect to have to spend at least 9 hours of work outside the classroom on this course each week.

CLASS PARTICIPATION

Students are expected to read the material assigned for each class in advance and to participate in class discussions. Class participation will count toward students' final grades.

HW ASSIGNMENTS, QUIZZES, EXAM, AND PROJECTS

Homework Assignments, Quizzes, and Final Exam

There will be 3 homework assignments, 2 quizzes, and a final exam.

Students' mastering of the material in HW1 (resp., HW2 and HW3) will be tested in Quiz1 (resp., Quiz2 and Final Exam). The final exam will be cumulative and will test the students' mastering of the material covered in the entire course.

Detailed descriptions of the HW assignments will be posted to the course webpage at the appropriate times during the semester.

Projects

There will be a total of 2 projects. These projects will include implementation, experimentation, and analysis of results. The projects may include also assigned readings and theoretical problems. In each project, students will work individually during the first stage of the project and then in assigned teams. Students will be required to provide both a written report and an oral (in-class) presentation describing their work on each of these projects. Both individual and group work will be graded.

For most of the projects, we will use the following programming languages / environments:

R. See R manuals.
Matlab. Available through the CCC.

Detailed descriptions of the projects will be posted to the course webpage at the appropriate times during the semester. An in-class presentation of each of the assignments will be required.

CLASS DISCUSSION FORUMS AND MAILING LIST

Class Discussion Forums: The main digital venue for communication outside the classroom will be the CS539 Discussion Forums provided by myWPI. To access these discussion forums, go to myWPI, click "CS539-F15-191: MACHINE LEARNING" under "My Courses", and then click on "Discussions" on the left hand-side bar.
Class Mailing List: There is also a mailing list for this class that will be used by the professor for general announcements, but not for class discussions This mailing list reaches the professor and all the students in the class.

Please make sure to read myWPI CS539 forums and email sent to the class mailing list constantly throughout the semester so that you don't miss any important course information.