We'll cover LDA in tw's meeting.   Here is the slide - https://drive.google.com/open?id=1KRoCA4vo9H9oJOl3iD-qRqIHl9qQq9vf

This is part of our deep dive into generative models which will eventually loops us back to BN but will also shade light on GAN approaches.    Here is some background and relevant resources - 

Generative models

Under the generative model approach we attempt to model the joint distribution p(x y). Given x and applying the Bayesian rule to our model we classify as y the y for which p(y | x) is largest.

A straight forward application of the Bayes rule is to attempt the estimation of probabilities in the Bayesian rule p(y | x) p(x) = p(x | y) p(y).   With the typical large number of dimensions of the vector x, density estimation of the required quantiles is really hard.   See the first 30 mins of https://m.youtube.com/watch?v=_m7TMkzZzus#fauxfullscreen for details.

As modeling the joint distribution p(x y) is hard simplifying assumption are introduced leading to different more concrete classification techniques.

LDA

LDA models each p(x | y) as a gaussian distribution.   This stat quest video describes how the average and standard deviation of the distribution are chosen to maximize the separation between the classes over the training set  https://m.youtube.com/watch?v=azXCzI57Yfc

The second 30 mins of this lecture derives LDA and explains what happens if the covariance of all class matrices are I https://m.youtube.com/watch?v=_m7TMkzZzus#

Here the estimation of a covariance matrices of a random vector is explained in detailhttps://en.m.wikipedia.org/wiki/Estimation_of_covariance_matrices 

See chapter 24 of the understanding book for a broader coverage of generation methods - https://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf

The background required for the Gaussian distribution and the covariance matrix is covered herehttp://cs229.stanford.edu/section/gaussians.pdf