Numerical Methods in Engineering

This course covers the theory and practice of numerical methods for a broad spectrum of engineering applications and data analyses. Topics include numerical calculus, linear algebra, and optimization. Students will be exposed to modern topics such as convolutional neural networks, compressed sensing, eigenfaces, stability, principal component analysis, k-means clustering, image segmentation, detection of a signal in the noise, and function fitting.

Credits

3 credits

Course Code

EE 643