CS 7150 — Deep Learning

4 semester hoursGraduateLectureusually offered: fall, springBostonOnlinePortland, MaineSilicon Valley, CAOnlineTraditional

Introduces deep learning, including the statistical learning framework, empirical risk minimization, loss function selection, fully connected layers, convolutional layers, pooling layers, batch normalization, multilayer perceptrons, convolutional neural networks, autoencoders, U-nets, residual networks, gradient descent, stochastic gradient descent, backpropagation, autograd, visualization of neural network features, robustness and adversarial examples, interpretability, continual learning, and applications in computer vision and natural language processing. Assumes students already have a basic knowledge of machine learning, optimization, linear algebra, and statistics.

Prerequisites

Offering history

TermSectionsEnrolledCapacityFullOpen seats/section
Fall 20231545992%5.0
Spring 20242186229%22.0
Fall 20241315853%27.0
Spring 20252638673%11.5
Fall 20252526580%6.5
Spring 2026411413783%5.8

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 49% · W 19% · Th 30% · F 16%

Common patterns: T (49% of sections), WF (16% of sections), MR (16% of sections), R (14% of sections), W (3% of sections), S (2% 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 6180, DADS 7305

Courses that list CS 7150 in their prerequisites.

Links

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