Machine Learning: Difference between revisions

From Adam Colton
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Line 21: Line 21:
* x - input variables
* x - input variables
* y - output variable
* y - output variable
* h - hypothesis (function h(x) = y)
Univariate linear regression


=== Cost Function ===
=== Cost Function ===

Revision as of 00:36, 23 April 2013

Week 1 - Intro

What is Machine Learning

  • Checkers playing computer (Arthur Samuel)

Supervised Learning

Right answer is provided. Types of problems

  • Regression
  • Classification

Unsupervised Learning

The data has no "correct answer"

Cocktail party algorithm

Week 1 - Linear Regression with One variable

Model Representation

Working with housing prices in Portland as our dataset.

Symbols used

  • m - number of training examples
  • x - input variables
  • y - output variable
  • h - hypothesis (function h(x) = y)

Univariate linear regression

Cost Function

Cost Function - Intuition : 1

Cost Function - Intuition : 2

Gradient Descent

Gradient Descent Intuition

Gradient Descent for Linear Regression