A little story. Recently in a job interview, I was asked to explain how
to derive the Principal Components from the Eigenvectors of a matrix.
Although, PCA can be useful for certain types of data, there are many
standard libraries which do the calculations. That is how I do it. The
last time I actually calculated eigenvectors directly was 1981. That
was my answer. Well, since this answer was less than satisfactory, I
did not get the job offer.
As a side note, after that interview, the company changed the job
description from "Machine learning Engineer" to "Machine Learning
Research Scientist". They decided they wanted someone to do research
instead of build production quality ML systems. They were apparently
following my "Machine Learning Skills Pyramid" from 2014.
Anyway, back to the point. Upon arriving home, a quick search on
YouTube uncovered an awesome resource for Machine Learning. The
fellow's name is Victor Lavrenko. His page is here:
The specific playlist for the EigenVectors question is here:
Be sure to watch all twelve videos in order for the full rundown on PCA
(about an 1.5 hours total)