DS 5220 — Supervised Machine Learning and Learning Theory

4 semester hoursGraduateLectureNUpath CENUpath WIusually offered: fall, springtypical days: T/FBostonPortland, MaineSilicon Valley, CATraditional

Introduces supervised machine learning, which is the study and design of algorithms that enable computers/machines to learn from experience or data, given examples of data with a known outcome of interest. Offers a broad view of models and algorithms for supervised decision making. 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-world data sets. Requires proficiency in a programming language such as Python, R, or MATLAB.

Offering history

TermSectionsEnrolledCapacityFullOpen seats/section
Fall 2023413621464%19.5
Spring 202427413057%28.0
Fall 202438613365%15.7
Spring 202538018144%33.7

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 16% · T 69% · W 25% · Th 6% · F 61%

Common patterns: TF (61% of sections), MW (16% of sections), W (9% of sections), T (8% of sections), R (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

CS 7150, DS 5500, EECE 5645, MATH 7339

Courses that list DS 5220 in their prerequisites.

Links

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