[MLE] W1 Introduction

Before get started

The blog is based on the machine learning course on Coursera, taught by Andrew Ng.
From today, i will begin my study trip on Machine learning, which will be labelled as MLE.
This course will last 11 weeks and today is week 1.
The application of machine learning is everywhere:

• Database mining
• Applications can’t program by hand
• Self-customizing program
• Understanding human learning(brain, real AI)

Introduction

What is Machine Learning

Two definitions of machine learning are offered:

• Arthur Samuel described it as: “the field of study that gives computers the ability to learn without being explicitly programmed.” This is an older, informal definition.
• Tom Mitchell provides a more modern definition: “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.”

If we raise a example of what is machine learning:

If we design a AlphaGo,
E = the experience of playing many games of checkers(chess)
T = the task of playing checkers.
P = the probability that the program will win the next game.

In general, any machine learning problem can be assigned to one of two broad classifications: Supervised Learning or Unsupervised Learning

Supervised Learning

In supervised learning, we are given a data set and already know what our correct output should look like, having the idea that there is a relationship between the input and the output.
Supervised learning problems are categorized into “regression” and “classification” problems.

Regression problems

In a regression problem, we are trying to predict results within a continuous output, meaning that we are trying to map input variables to some continuous function.

For example, this problem: housing price prediction. We are given a set of data which related to the size and price of a house. And we predict the price of one house with some size. It is regression, because we are predicting a continuous result.

Classification problem

In a classification problem, we are instead trying to predict results in a discrete output. In other words, we are trying to map input variables into discrete categories.

For example this breast cancer problem. From some attributes, we predict whether the cancer is malignant or benign. Now this attribute is tumor size, and it can be anything. But in classification, what we predict or decide is some discrete thing(whether A or B).

Comparison

Example 1:

• Given data about the size of houses on the real estate market, try to predict their price. Price as a function of size is a continuous output, so this is a regression problem.
  
• We could turn this example into a classification problem by instead making our output about whether the house “sells for more or less than the asking price.” Here we are classifying the houses based on price into two discrete categories.
  

Example 2:

• (a) Regression - Given a picture of Male/Female, We have to predict his/her age on the basis of given picture.
  
• (b) Classification - Given a picture of Male/Female, We have to predict Whether He/She is of High school, College, Graduate age. Another Example for Classification - Banks have to decide whether or not to give a loan to someone on the basis of his credit history.
  

Unsupervised learning

Unsupervised learning, on the other hand, allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don’t necessarily know the effect of the variables.

We can not learn from any data but the machine should learn directly to give us results

We can derive this structure by clustering the data based on relationships among the variables in the data.

With unsupervised learning there is no feedback based on the prediction results, i.e., there is no teacher to correct you.
It can also be divided into clustering and non-clustering.
Example:

Clustering

Clustering: Take a collection of 1000 essays written on the US Economy, and find a way to automatically group these essays into a small number that are somehow similar or related by different variables, such as word frequency, sentence length, page count, and so on.
Another example is google news, google collects all the news from different newspaper or website which uses clustering.

Non-clustering

Non-clustering: The “Cocktail Party Algorithm”, which can find structure in messy data (such as the identification of individual voices and music from a mesh of sounds at a cocktail party (https://en.wikipedia.org/wiki/Cocktail_party_effect) ). Here is an answer on Quora to enhance your understanding. : https://www.quora.com/What-is-the-difference-between-supervised-and-unsupervised-learning-algorithms ?
From the class, we know that in matlab, we only need one line code to solve cocktail party algorithm, awesome!