Machine Learning Series, Part -1
Machine Learning : Basics
*This series is inspired from online lectures of Andrew Ng on machine learning.
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."
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.
In general, any machine learning problem can be assigned to one of two broad classifications:
Supervised learning and Unsupervised learning & others(Reinforcement learning, recommender systems etc.)
Supervised learning
In supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines), 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.. An example of this would be learning to predict whether an email is spam if given a million emails, each of which is labeled as “spam” or “not spam”.
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.
Examples:
- 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.
- Given a picture of a person, we have to predict their age on the basis of the given picture
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.
Examples:
- Given a patient with a tumor, we have to predict whether the tumor is malignant or benign.
- We could turn above real estate 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.
Unsupervised Learning
In an unsupervised learning (clustering, dimensionality reduction, kernel methods), the algorithm can find trends in the data it is given, without looking for some specific “correct answer”. 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.
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. (i.e. identifying individual voices and music from a mesh of sounds at a cocktail party).
This is just the short introduction to the machine learning, in next article we'll discuss about supervised learning and it's first basic algorithm implementation in Matlab.
