CS 6140 — Machine Learning

4 semester hoursGraduateLectureNUpath CEusually offered: fall, springArlington, VABostonMiami, FLOakland, CAOnlinePortland, MaineSeattle, WASilicon Valley, CAVancouver, CanadaHybridOnlineTraditional

Provides a broad look at a variety of techniques used in machine learning and data mining, and also examines issues associated with their use. Topics include algorithms for supervised learning including decision tree induction, artificial neural networks, instance-based learning, probabilistic methods, and support vector machines; unsupervised learning; and reinforcement learning. Also covers computational learning theory and other methods for analyzing and measuring the performance of learning algorithms. Course work includes a programming term project.

Prerequisites

Offering history

TermSectionsEnrolledCapacityFullOpen seats/section
Fall 2023618228963%17.8
Spring 2024516123868%15.4
Summer A 2024174018%33.0
Fall 2024619428967%15.8
Spring 2025930036183%6.8
Summer A 20251112544%14.0
Fall 2025935348473%14.6
Spring 20261142759871%15.5

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 23% · T 35% · W 23% · Th 9% · F 23%

Common patterns: TF (21% of sections), async (20% of sections), T (13% of sections), M (12% of sections), W (11% of sections), MW (11% of sections) — in patterns, R means Thursday

Professors

Fall

Spring

Summer A

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

Unlocks

CHME 6580, CS 5170, CS 6170, CS 6180, CS 7140, CS 7150, DADS 7305, DS 5500, EECE 5668, EECE 7397, MATH 7339

Courses that list CS 6140 in their prerequisites.

Links

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