# Machine Learning Week6 Advice for Applying Machine Learning

## Evaluating a Learning Algorithm

### Deciding what to try next

• We know many learning algorithm
• But,how to choose the best algorithm to explore the various techniques
• Here we focus deciding what avenues to try

# Machine Learning Week5 Neural Networks Learning

## Cost Function and Backpropagation

### Cost Function

• L = total number of layers in the network
• sls = number of units (not counting bias unit) in layer l
• K = number of output units/classes

# Machine Learning Week4 Neural Networks Representation

## Motivations

### Non-linear Hypotheses

#### Why do we need neural networks?

• Consider a supervised learning classification problem
• logistic regression
• g as usual is sigmoid function
• And, if you include enough polynomial terms then, you know, maybe you can get a hypotheses.
• However, this problem is just about two features x1 and x2, many machine learning problems would have a lot more features.
• e.g. our housing example
• 100 house features, predict odds of a house being sold in the next 6 months
• Here, if you included all the quadratic terms (second order)
• There are lots of them (x12 ,x1x2, x1x4 ..., x1x100)
• For the case of n = 100, you have about 5000 features
• Number of features grows O(n2)
• Not a good way to build classifiers when n is large

# Machine Learning Week3 Logistic Regression

## Classification and Representation

### Classification

• y is a discrete value

• Variable in these problems is Y

• Y is either 0 or 1
• 0 = negative class (absence of something)
• 1 = positive class (presence of something)
• Later look at multiclass classification problem

# Machine Learning Week2 Linear Regression with Multiple Variables

## Multivariate Linear Regression

### Multiple Features

• Multiple variables = multiple features
• If in a new scheme we have more features to predict the price of the house
• x1, x2, x3, x4 are the four features
• x1 - size (feet squared)
• x2 - Number of bedrooms
• x3 - Number of floors
• x4 - Age of home (years)
• y is the output variable (price)

# Machine Learning Week1 Introduction

## Introduction

### What is machine learning?

• Two definitions:
• Arthur Samuel(1959)
• "The field of study that gives computers the ability to learn without being explicitly programmed."
• This is an older, informal definition.
• Tom Mitchell(1999)
• "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E."

The checkers example.
• E = the experience of playing many games of checkers
• T = the task of playing checkers.
• P = the probability that the program will win the next game.
• Several types of learning algorithms
• Supervised learning
• Unsupervised learning
• Reinforcement learning
• Recommender systems