What is Machine Learning?

Arthur Samuel described: " 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. "

Example: playing checkers.

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.

Three main different categories:

• Supervised Learning – Train Me!
• Unsupervised Learning – I am self sufficient in learning
• Reinforcement Learning – My life My rules! (Hit & Trial)

  Supervised machine 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. 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. 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.

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 a person, we have to predict their age on the basis of the given picture

(b) Classification - Given a patient with a tumor, we have to predict whether the tumor is malignant or benign.

some other examples:

• loan approved/rejected
• spam detection
• image classification
• medical diagnostic system
• stock price prediction

Unsupervised machine learning : 

      Unsupervised learning 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 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.

Example:

Clustering: Take a collection of 1,000,000 different genes, and find a way to automatically group these genes into groups that are somehow similar or related by different variables, such as lifespan, location, roles, and so on.

Non-clustering: The "Cocktail Party Algorithm", allows you to find structure in a chaotic environment. 

some examples:

• fraud detection
• image segmentation
• customer segmentation
• market analysis

Reinforcement machine learning:

Decision made by the system on the basis of the reward it received for the last action it performed.
It usually learn optimal actions through trial and error.

some examples: 

• robotics – where a robot can learn to avoid collisions by receiving negative feedback after bumping into obstacles.
• video games – where trial and error reveals specific movements that can shoot up a player’s rewards.