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Course Details

Spring 2016-2017
COS 424 / SML 302   Graded A-F, P/D/F, Audit

Fundamentals of Machine Learning

Barbara Engelhardt

Computers have made it possible, even easy, to collect vast amounts of data from a wide variety of sources. It is not always clear, however, how to use those data, and how to extract useful information from them. This problem is faced in a tremendous range of business and scientific applications. This course will focus on some of the most useful approaches to the problem of analyzing large complex data sets, exploring both theoretical foundations and practical applications. Students will gain experience analyzing several types of data, including text, images, and biological data.

Reading/Writing assignments:
Written exercises, programming and data-exploration projects; reading from text and primary sources. Student is expected to read 20 pages per week.

Programming Assignments - 60%
Quizzes - 10%
Other (See Instructor) - 30%

Other Requirements:
Not Open to Freshmen.

Prerequisites and Restrictions:
MAT 202 and COS 126 or equivalent or permission of instructor..

Other information:
Topics will include classification, clustering, prediction, and dimension reduction. Final Project is 30% of grade.


Schedule/Classroom assignment:

Class numberSectionTimeDaysRoomEnrollmentStatus
41546 L01 11:00 am - 12:20 pm T Th   Computer Science Building   104   Enrolled:105 Limit:140
43431 P01 11:00 am - 11:50 am F   Computer Science Building   105   Enrolled:49 Limit:70
43615 P02 1:30 pm - 2:20 pm F   Computer Science Building   105   Enrolled:56 Limit:70