Intro to Kernel Density Estimation 

This video gives a brief, graphical introduction to kernel density estimation. Many plots are shown, all created using Python and the KDEpy library (github.com/tommyod/KDEpy). A .pdf of the presentation may be found here: github.com/tommyod/KDEpy/blob/master/docs/presentation/kde_presentation.pdf
Contents
00:22 - What is kernel density estimation?
01:27 - Kernel functions
03:27 - Bandwidth
04:30 - Silverman's rule of thumb
05:19 - Improved Sheather Jones
06:10 - Weighting the data
07:30 - Bounded domains and reflections
09:18 - Kernel density estimation in higher dimensions
10:02 - The choice of norm
11:11 - Example of 2D kernel density estimation
12:36 - A fast algorithm using linear binning and convolution
15:30 - 2D linear binning
16:18 - KDEpy - software for kernel density estimation in Python
16:51 - References