# Machine Learning Algorithms

m = number of training examples

n = number of features

Cost: $J_{train}(\theta)=\frac{1}{2m}\sum_{i=1}^mh_\theta(x^{(i)}-y^{(i)})^2$

Repeat{ $\theta_j := \theta_j - \alpha \frac{1}{m}\sum_{i=1}^mh_\theta(x^{(i)}-y^{(i)})x_j^{(i)}$(j=0:n) }for

$cost(\theta,(x^{(i)},y^{(i)})) = \frac{1}{2}h_\theta(x^{(i)}-y^{(i)})^2$

Cost $J_{train}(\\theta)=\\frac{1}{m}\\sum_{i=1}^mcost(\\theta,(x^{(i)},y^{(i)}))$

 Repeat{    for i=1:m{      θj* := *θj − αhθ(x(i) − y(i))x**j(i)(j=0:n)    }  }for  - usually 1-10

### Convergence Stochastic gradient descent cost/nr of iterations top left - expected; top right - larger number of iterations used for average bottom left - algorithm not doing well bottom right - divergence

Compute ost(θ, (x(i), y(i))) before each update to θ. Every plot the cost average over the last iterations

Imagine b(number of examples in batch)=10, m=1000

Repeat{   for i=1,11,21,...991{     $\theta_j := \theta_j - \alpha \frac{1}{m}\sum_{k=i}^{i+9}h_\theta(x^{(i)}-y^{(i)})x_j^{(i)}$(j=0:n)   } }for

## Online learning

For instance searches on a website, or purchases on a website.

 Repeat{    Get(x,y)      Update θ      θj* := *θj − αhθ(x − y)x**j(j=0:n)  }forever

Powerful as it can adapt to user preference changes in time.

## Map Reduce and Parallelism

We map the test test set into a number of chunks depending on how many machines/cpu cores we want to use, then we process each chunk in parallel, then add up the result on one of the machines/cpu cores.