DS 5230 — Unsupervised Machine Learning and Data Mining

4 semester hoursGraduateLectureNUpath CENUpath WIusually offered: fall, springArlington, VABostonPortland, MaineSilicon Valley, CALive CastTraditional

Introduces unsupervised machine learning and data mining, which is the process of discovering and summarizing patterns from large amounts of data, without examples of data with a known outcome of interest. Offers a broad view of models and algorithms for unsupervised data exploration. Discusses the methodological foundations behind the models and the algorithms, as well as issues of practical implementation and use, and techniques for assessing the performance. Includes a term project involving programming and/or work with real-life data sets. Requires proficiency in a programming language such as Python, R, or MATLAB.

Offering history

TermSectionsEnrolledCapacityFullOpen seats/section
Fall 202329014064%25.0
Spring 2024514623263%17.2
Fall 202449318949%24.0
Spring 2025414219473%13.0
Fall 20253286245%11.3
Spring 20262434498%0.5

Snapshots from scheduled scrapes — not live seat availability. "Full" can exceed 100% when sections over-enroll.

Meeting times

Share of recent sections by weekday: M 29% · T 49% · W 37% · Th 22% · F 5%

Common patterns: T (42% of sections), W (13% of sections), MWR (12% of sections), MW (12% of sections), R (9% of sections), M (6% of sections) — in patterns, R means Thursday

Professors

Fall

Spring

Percentages are each professor's average share of the season's enrolled students in recent terms.

Unlocks

DS 5500, EECE 5645

Courses that list DS 5230 in their prerequisites.

Links

Official catalog (DS course descriptions) · Student reviews on RateMyHusky · All DS courses · Plan it at numap.app