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Introduction to Machine Learning
Arthur Samuel defines ML as “the field of study that gives computers the ability to learn without being explicitly programmed.”
Tom Mitchell defines ML as “[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.
You are given a dataset but do not have an idea of what the output will be. In some cases relationships are determined to “cluster” variables together. In other cases, unsupervised learning is used to separate things in a chaotic environment.
Clustering Example: Taking a list of NBA player data and stats and grouping players that do similar things together. Non-Clustering Example: Take a recording of people speaking and isolating the individual voices.
You are given a data set and already know what our correct output should look like, getting a sense of the relationship between the output and input. Essential, you are given the “the right answers”.
There are two kinds of supervised learning problems REGRESSION & CLASSIFICATION. Regression is trying to determining the results in a continuous output. Classification is outputting inputs into categories.