DS 4400 — Machine Learning

4 semester hoursUndergraduateLectureNUpath ADNUpath CENUpath WIusually offered: fall, springtypical days: T/FBostonOnlineOnlineTraditional

Introduces supervised and unsupervised predictions, modeling, and essential machine learning concepts. Uses tools and libraries to analyze datasets, build predictive models, and evaluate the fit of the models. Covers common learning algorithms such as regression; classification (including support vector machines and logistic regression); dimensionality reduction (including principal-component analysis); clustering, gradient descent, regularization techniques, multiclass data, and algorithms; boosting; and decision trees. Studies computational aspects of probability, statistics, and linear algebra that support algorithms, including sampling theory and computational learning. Applies concepts to common problem domains.

Prerequisites

Offering history

TermSectionsEnrolledCapacityFullOpen seats/section
Summer B 20231295058%21.0
Fall 2023212514089%7.5
Spring 20243238234102%0.0
Fall 2024416433948%43.8
Spring 2025432840681%19.5
Fall 2025424932078%17.8
Spring 2026327227499%0.7

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 34% · T 55% · W 8% · Th 29% · F 53%

Common patterns: TF (46% of sections), MR (23% of sections), T (7% of sections), F (7% of sections), M (5% of sections), MW (4% of sections) — in patterns, R means Thursday

Professors

Fall

Spring

Summer B

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

Unlocks

CS 4120, CY 4100, DS 4420, DS 4440, EECE 5668

Courses that list DS 4400 in their prerequisites.

Links

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