Supervised learning and unsupervised learning

Supervised Learning

Given an algorithm that requires some data sets already have the correct answer. For example, given the price data set. Supervised learning is also called regression.

Example: House price prediction, cancer prediction

Unsupervised Learning

The sample set is not labeled, and a set of unlabeled data is divided into multiple clusters

Example: Organizing computer clusters, social network analysis

Cocktail Party Issues

Extracting effective information from background noise.

[W,s,v]=svd ((Repmat (sum (x.*x,1), size (x,1), 1). *x) *x ');

linear regression

For example, prices are as follows:

$x _{1}^{(i)}$ represents the living area of the first house, $x _{2}^{(i)}$ represents the number of bedrooms in the first house, so X is a two-dimensional vector.

Define hypothetical functions: $h _{\theta} (x) $=$\theta_{0}$+$\theta_{1}$ $x _{1}$+$\theta_{2}$ $x _{2}$

$h (x) =\sum_{i=0}^{n}\theta_{i}x_{i}$ = $\theta ^{t}x$

The next question is how to solve the $\theta$ value

Define the following loss function to indicate the proximity of $h (X^{i}) $ to $y^{i}$

$J (\theta) =\frac{1}{2}\sum_{i=1}^{m} (H_{\theta} (x^{(i)})-y^{{i}) ^{2}$

LMS Algorithm

Supervised learning and unsupervised learning