Understanding Multivariate Gaussian Distribution (Machine Learning Fundamentals)

#gaussiandistribution #machinelearning #statistics
In this video, we will understand the intuition and maths behind the Multivariate Gaussian/Normal Distribution. We will be doing a walkthrough from Stanford CS229 course document.

⏩ OUTLINE:
0:00 - Introduction and Formula breakdown
2:35 - Relationship between Multivariate Gaussians and Univariate Gaussians
6:00 - Covariance Matrix
8:38 - Diagonal Covariance Matrix
11:10 - Shape of Isocontours
12:35 - Heatmap density view

⏩ Organisation: Stanford University

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[_jiwi]

I really like how you are making an effort to explain the intuition behind the concepts by explaining the meanings within the equations. Only a few ppl do that.

[kunalmenavlikar]

Really a great explaination. I really liked the part you related the formulation of gaussian curve with the iso-contours.

[arhan-]

I'm an undergrad at Berkeley currently taking ML and you just saved my ass on this midterm

[reddityt]

some input on how the eigen values of the covariances matrix would give a better intuition for the ellipses

[Orioricrafts]

At @11.55 I think the whole term is supposed to be r1 squared and not squared root of r1.

Btw wonderful explanation 👍