IE 7300 — Statistical Learning for Engineering

4 semester hoursGraduateLectureusually offered: fall, springtypical days: TBostonOnlineSeattle, WAVancouver, CanadaOnlineTraditional

Covers statistical models and methods for data analytics. Reviews fundamentals and key concepts in statistical inferences and learning theory. Presents important statistical learning techniques and algorithms for implementation. Discusses ordinary least square for regression, overfitting and regularization, generalized linear models, and nonlinear and nonparametric models. Applies Bayesian statistics for classification problems. Offers theoretical aspects including the statistical learning framework, conditional probabilistic methods, and their applications in several areas including manufacturing, healthcare, and business.

Prerequisites

Offering history

TermSectionsEnrolledCapacityFullOpen seats/section
Fall 2023514118377%8.4
Spring 20241529555%43.0
Summer A 2024172825%21.0
Fall 20241217528%54.0
Spring 20252455508%252.5

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 30% · T 55% · W 9% · Th 0% · F 0%

Common patterns: T (55% of sections), M (25% of sections), async (11% of sections), MW (6% of sections), W (3% 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, DADS 7305

Courses that list IE 7300 in their prerequisites.

Links

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