// Databricks notebook source exported at Sat, 18 Jun 2016 23:27:13 UTC

Scalable Data Science

prepared by Raazesh Sainudiin and Sivanand Sivaram

supported by and

The html source url of this databricks notebook and its recorded Uji :

Let us visit an interactive visual cognitive tool for the basics ideas in linear regression:

• http://setosa.io/ev/ordinary-least-squares-regression/

The following video is a very concise and thorough treatment of linear regression for those who have taken the 200-level linear algebra. Others can fully understand it with some effort and revisiting.

Linear Regression by Ameet Talwalkar in BerkeleyX: CS190.1x Scalable Machine Learning

(watch now 11:13):

Ridge regression has a Bayesian interpretation where the weights have a zero-mean Gaussian prior. See 7.5 in Murphy's Machine Learning: A Probabilistic Perspective for details.

Please take notes in mark-down if you want.

For latex math within markdown you can do the following for in-line maths: $\mathbf{A}_{i,j} \in \mathbb{R}^1$. And to write maths in display mode do the following:

$\mathbf{A} \in \mathbb{R}^{m \times d}$

You will need to write such notes for your final project presentation!

MillonSongs Ridge Regression by Ameet Talwalkar in BerkeleyX: CS190.1x Scalable Machine Learning

(watch later 7:47):

Covers the training, test and validation and grid search... ridger regression...

Take your own notes if you like.

Gradient Descent by Ameet Talwalkar in BerkeleyX: CS190.1x Scalable Machine Learning

(watch now 11:19):

Please take notes if you want to.

Scalable Data Science

prepared by Raazesh Sainudiin and Sivanand Sivaram

supported by and