Rose-Hulman Institute of Technology · 4 credits · UGRD
Catalog
Section information text: An introduction to deep learning using both fully-connected and convolutional neural networks. Topics include: least squares estimation and mean square error, maximum likelihood estimation and cross-entropy, convexity, gradient descent and stochastic gradient descent algorithms, multivariate chain rule and gradient computation using back propagation, linear vs nonlinear operations, convolution, over-fitting vs under-fitting and hyper-parameter optimization, L2, early stopping and dropout regularization, data augmentation and transfer learning. Same as MA416.
Recent sections
Fall 2026 · 4 sections
Sep 3 – Nov 19
Section
Days & time
Location
Instructor
02Class #1231
Days & timeMo Tu Th Fr · 9:00 – 9:50 AM
LocationOlin Hall O167
InstructorBoutell, Matt
02 notes: Instructional method: Lecture
01Class #1232
Days & timeTime not published
LocationLecture
InstructorTBA
01 notes: Instructor not yet assigned in the official SIS; Instructional method: Lecture