What is machine learning?
Two definitions of machine learning are offered.
Arthur Samuel version:
A field of study that gives computers the ability to learn without being explicitly programmed.
Tom Mitchell version:
A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on 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.
Machine learning algorithms
In general, any machine learning problem can be assigned to one of two broad classifications:
- Supervised learning
- Unsupervised learning
There are also some others: Reinforcement learning, Recommender system
Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. It infers a function from labeled training data consisting of a set of training examples, for every example in the training data set, the correct answer is already given.
There are two types of supervised learning problems:
In a regression problem, the supervised learning algorithm is trying to predict results within a continuous output, meaning that it is trying to map input variables to some continuous function.
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.
In a classification problem, the supervised learning algorithm is instead trying to predict results within a discrete output, meaning that it is trying to map input variables to discrete categories.
Classification - Given a patient with a tumor, we have to predict whether the tumor is malignant or benign.
Free online course offered by Stanford: https://www.coursera.org/learn/machine-learning