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Rose-Hulman Institute of Technology · 4 credits · UGRD

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 CSSE416.
Fall 2026 · 4 sections
Sep 3 – Nov 19
| Section | Days & time | Location | Instructor |
|---|---|---|---|
| 01Class #1398 | Time not published | Lecture | TBA |
| 01 notes: Instructor not yet assigned in the official SIS; Instructional method: Lecture | |||
| 02Class #1404 | |||
| Olin Hall O167 |
| Boutell, Matt |
| 02 notes: Instructional method: Lecture |
| 03Class #1414 | Mo Tu Th Fr · 10:00 – 10:50 AM | Olin Hall O167 | Boutell, Matt |
|---|---|---|---|
| 03 notes: Instructional method: Lecture | |||
| 04Class #1415 | Mo Tu Th Fr · 11:00 – 11:50 AM | Olin Hall O167 | Petrik, Rachel |
|---|---|---|---|
| 04 notes: Instructional method: Lecture | |||