What is Machine Learning?
“A computer program 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.”
Two Types of 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.
We try to predict results within a continuous output, meaning that we are trying to map input variables to some continuous function.
ex) Given data about the size of houses on the real estate market, try to predict their price.
We try to predict results in a discrete output. In other words, we are trying to map input variables into discrete categories.
ex) Given a picture of a person, try to predict Whether he/she is of High school, College, Graduate age.
It 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.
‘Clustering’ is one type of unsupervised learning.
ex) 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.