IE 7615 — Deep Learning for AI

4 semester hoursGraduateLectureusually offered: fall, springtypical days: ThBostonSeattle, WAVancouver, CanadaLive CastTraditional

Presents an in-depth exploration of modern deep learning architectures and methodologies that drive today’s AI systems. Examines both foundational and advanced models, with attention to theoretical understanding and practical implementation through programming assignments and projects. Topics include convolutional and recurrent networks, generative models, transformers, diffusion models, multimodal learning, and emerging approaches such as world models. Emphasizes mathematical rigor and real-world applications across computer vision, natural language processing, and generative AI.

Prerequisites

Offering history

TermSectionsEnrolledCapacityFullOpen seats/section
Fall 202336311853%18.3
Spring 20242609663%18.0
Fall 202437011660%15.3
Spring 20252409642%28.0
Summer A 202512248%22.0
Fall 20252618473%11.5
Spring 202644812638%19.5

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 26% · T 6% · W 0% · Th 67% · F 29%

Common patterns: R (41% of sections), F (28% of sections), MR (25% of sections), T (4% of sections), TR (1% of sections), TF (1% 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

DADS 7305

Courses that list IE 7615 in their prerequisites.

Links

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