Multivariate Probability Models in Machine Learning [D]

Hello Folks, we start our discussion on Lecture 10 of Probabilistic Machine Learning, now starting with Probability Multivariate Models.

Univariate models are toy cases, in real life, ML models are multivariate.

To understand dependence of more than one variables on each other we study ideas as Covariance, Correlations, we delve ourselves into the interesting concept of Simpson’s Paradox, with an example. We define the Multivariate Gaussian distribution, understand the level sets(curves) that we see in our computers while plotting, and gain insights into the geometric shape of the Gaussian density by using “Mahalanobis distance”.

Mathematical foundations are extremely important, in that they make an ML engineer, data scientist stand out. These concepts are becoming so ubiquitous today, that folks from all backgrounds of engineering are interested in the mathematics behind these algorithms.

I hope the learning community finds it helpful, and suggestions are always welcomed.

These are FREE lectures.
Link in comments