Lecture 4 | Machine Learning (Stanford)
Why Cross Validated Stack Exchange recommends it
Cross Validated users link it when learners need Ng's clear explanation of exponential family distributions and the motivation behind generalized linear models.
Recommended by 2 people across 2 different months, 2014 to 2020.
What it is
Lecture 4 of Andrew Ng's Stanford CS229 Machine Learning course (2008 recording). It covers Newton's method, exponential families, and generalized linear models, building on the earlier supervised learning lectures.
What people said
Recommends MJW · 2014-08 · Cross Validated Stack Exchange · on “What is meant by the term 'exponential family'? Why it is named so?”
Just a quick tip: [this video] I found this lecture from Andrew Ng very useful to get the concept of exponential family distributions down,.. like sweet Kool-Aid.
Recommends figs_and_nuts · 2020-03 · Cross Validated Stack Exchange · on “What is the idea behind generalized linear models?”
I am watching andrew ng's video lectures [this video] on machine learning. I am trying to understand what is even the point of generalized linear models. I understand what goes on step by step in deriving things related to generalized models but as a whole it just looks like a lot of much-ado-about-nothing. Here is my understanding (In reference to modeling a bernoull distribution): We make the assumption that the output is distributed according to a bernoulli distribution with parameter $\phi$ $p(y;\phi) = \phi^{y}(1-\phi)^{1-y}$ $\tag 1$ We use craft to represent the same equation such that it is in the structure of $ExponentialFamily(\eta): p(y;\eta) = b(y)(\eta^{T}T(y)-a(y)$ where $T(y)$ is a
Cites GrowinMan · 2015-07 · Cross Validated Stack Exchange · on “Deriving Linear Regression update rule: Why do we set the derivative of cost funtion to 0 to minimize it?”
(This question may be more about Math in general but I have a specific context in mind and hence am asking this here as opposed to math.stackexchange) I'm going through Andrew Ng's lecture notes [LINK] on Machine Learning. While deriving the update rule for linear regression, the cost function's derivative is set to 0. I understand the cost function needs to be minimized. But setting the derivative as 0 means either you're considering the maxima, or the minima. So my question is: How can we be sure that setting the derivative to zero in this case will lead to considering the minima and not the maxima? (I'm not sure, but my guess is that it's because we already somehow know that the cost func