Machine Learning

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

h_Θ(x) = Θ₀ + Θ₁x

sum of square error as our cost function. He's using 1/2m rather than 1/m, I'm not sure why.

Cost Function - Intuition : 1

Demo of technique.

Cost Function - Intuition : 2

Contour plots.

Gradient Descent

Θ_j = Θ_j - α (d/dΘ_j) * J(Θ₀, Θ₁)

Gradient Descent Intuition

Explaining gradient descent.

Gradient Descent for Linear Regression

• Θ₀ = Θ₀ - α(1/m) Σ^m (h_Θ(x_i) - y_i)
• Θ₁ = Θ₁ - α(1/m) Σ^m (h_Θ(x_i) - y_i) * x_i

Linear Regression with Multiple Variables - Week 2

Gradient Descent for Multiple Variables

• Parameters: Θ₀,Θ₁,...,Θ_n
• Cost Function: J( Θ₀,Θ₁,...,Θ_n ) or simply J(Θ)

Much like before, gradient descent is calculated for all Θs before being applied. To make the formula more uniform we set x_0,i = 1 for all i then the formula becomes

 Θj = Θj - (α/m) * Σi=1m (h(Θ, xi) - yi) * xi,j

Note: I may have my notation for rows and columns mixed up in the formula - double check before using.

Gradient Descent in Practice 1 - Feature Scaling

Try to make features on a similar scale. In the house example, if our features are size 0-2000ft² and bedrooms (1-5), it will take a long time to converge, because the first feature is three orders of magnitude larger. Either scale bedrooms up or the ft² down. A simple way to do this is scale all features filling the range (-1, 1). If future data lies outside this range, it is not a problem. If future data falls way outside this range, you'll have bigger problems.

Try and set the mean to 0. This is "mean normalization".

Of course, none of this applies to x₀ = 1.

Gradient Descent in Practice 2 - Learning Rate

The correct α should cause J to decrease continuously. There is not a good automatic way to choose α at this time, but by looking at the iterations graph you can see that the slope is decreasing quickly enough, but not increasing.

Features and Polynomial Regression

You are free to make new features from the features you are given. This includes taking the root, log or square of a feature. Continue to use scaling.

Normal Equation

Using matrices to solve the equation. Feature scaling is not necessary using the normal method.

Gradient descent does not work as well as the number of features becomes very large (because it has an N^2 component). Somewhere around 10K features is a good point to cross over and start using gradient descent.

Normal Equation Noninvertibility

Some matrices are non-invertible.

Octave Tutorial - Week 2

Basic Operations

• ~= Not equal

Semi-colon suppresses echo

To define a matrix

 A = [1 2; 3 4; 5 6]

so the delimiter between cols is just a space and the delimiter between rows is semi-colon.

A vector range can be defined with

 V = start:step:stop

such as

 V = 1:1:5

Giving us the same as

 V = [1 2 3 4 5]

note the inclusivity.

We can generate a matrix of all ones with

 M = ones(h,w)

with w be width and h being height. Also works with zeros, rand (uniform distribution), randn (normal distribution).

Similarly we can do

 eye(x)

producing a square identity matrix.

Print a histogram (bar graph) with

 hist(v[, d])

where v is a vector and the optional d is the number of divisions (bars).

Finally,

 help command