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Course Evaluation Results

Course Details

Spring 2018-2019
* COS 424 / SML 302   Graded A-F, P/D/F, Audit

Fundamentals of Machine Learning

Barbara E 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:
Programming and data-exploration projects; reading from text and primary sources. Student is expected to read 20 pages per week.

Requirements/Grading:
Programming Assignments - 60%
Quizzes - 10%
Other (See Instructor) - 30%

Other Requirements:
Not Open to First Year Undergraduates.

Prerequisites and Restrictions:
MAT 202 and ORF 245 or ORF 309 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.

Website:  http://www.cs.princeton.edu/courses/archive/spring19/cos424

Schedule/Classroom assignment:

Class numberSectionTimeDaysRoomEnrollmentStatus
42259 L01 08:30:00 am - 09:50:00 am M W   Friend Center   101   Enrolled:169 Limit:215
42260 P01 10:00:00 am - 10:50:00 am Th   Computer Science Building   105   Enrolled:72 Limit:72 Closed
42261 P02 03:30:00 pm - 04:20:00 pm F   Computer Science Building   105   Enrolled:75 Limit:75 Closed
42516 P03 03:30:00 pm - 04:20:00 pm F        Enrolled:0 Limit:72 Closed
42737 P04 02:30:00 pm - 03:20:00 pm W   Computer Science Building   105   Enrolled:22 Limit:50