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The Exponential Family includes almost all of the most frequently encountered distributions. In this video, I show how it functions as such a wide reaching generalization. But it's more than a generalization. This video will set up part 2 where I all the useful properties that follow from this clever mathematical device.

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CONTENTS
0:00 Introducing the Exponential Family
1:11 The Normal Distribution as a special case of the Exponential Family
3:40 Stating the Exponential Family Precisely
9:00 The Bernoulli Distribution as a case of the Exponential Family
10:50 The Multinoulli Distribution as a case of the Exponential Family
14:00 Different Choices, Different Distributions

Chapter 9 from [1] gives a nice overview of the Exponential Family and it's usefulness within Machine Learning. [2] was a useful additional perspective and made clear how to interpret the measure of x. [3] gave me the list of all distributions.

SOURCES
[1] K. P. Murphy. Machine Learning: A Probabilistic Perspective, MIT Press, 2012
[2] M. I. Jordan, Exponential Family: Basics, University of California, Berkeley, https://people.eecs.berkeley.edu/~jor...
[3] The Exponential Family, Wikipedia, https://en.wikipedia.org/wiki/Exponen...