Uploaded May 2021 | Updated September 2026, 1 day ago
In this lecture we coded up the Gaussian RBF interpolation routine for high dimensional systems. This code was vectorized in MATLAB which made it magnitudes quicker than methods using for loops.
Code: bitbucket.org/joelrosenfeld/rbf-and-kernel-interpolation
Music:
Come 2gether by Ooyy
Sunrise in Paris by Dan Henig
You're a Believer by Stonekeepers
Guardians + Tek by Craig Hardgrove
0:00 Start and Introduction
05:53 MATLAB Start - Gram Matrix
16:25 Speeding up the evaluation of the approximation
In this lecture we coded up the Gaussian RBF interpolation routine for high dimensional systems. This code was vectorized in MATLAB which made it magnitudes quicker than methods using for loops.
Code: bitbucket.org/joelrosenfeld/rbf-and-kernel-interpolation
Music:
Come 2gether by Ooyy
Sunrise in Paris by Dan Henig
You're a Believer by Stonekeepers
Guardians + Tek by Craig Hardgrove
0:00 Start and Introduction
05:53 MATLAB Start - Gram Matrix
16:25 Speeding up the evaluation of the approximation










![Getting caught with facial recognition - How SVDs are used in Facial Recognition Software
This video builds on the SVD concepts of the previous videos, where I talk about the algorithm from the paper Eigenfaces for Recognition. These tools are used everywhere from law enforcement (such as tracking down the rioters at the Capitol) to unlocking your cell phone.
Followup Video: https://youtu.be/WnsKGBy1PXQ
Music:
Come 2gether by Ooyy
Guardians + Tek by Craig Hardgrove
Images:
Lectern Photo by Win McNamee/Getty Images (Used under Fair Use for an educational video)
Face Data obtained from http://www.databookuw.com/ the readme states
This data is modified from the Extended Yale Face Database B. If using this data, please also cite the following two papers:
[1] A. S. Georghiades, P. N. Belhumeur, and D. J. Kriegman, From few to many: Illumination cone models for face recognition under variable lighting and pose, IEEE Trans. Pattern Anal. Mach. Intell., 23 (2001), pp. 643–660.
[2] K. Lee, J. Ho, and D. J. Kriegman, Acquiring linear subspaces for face recognition under variable lighting, IEEE Trans. Pattern Anal. Mach. Intell., 27 (2005), pp. 684–698.
Link to website: vision.ucsd.edu/content/extended-yale-face-database-b-b Getting caught with facial recognition - How SVDs are used in Facial Recognition Software](https://i.ytimg.com/vi/b-5jg5VvUEo/mqdefault.jpg)