Introduction to Machine Learning

Definition of Machine Learning: Arthur Samuel, an early American leader in the field of computer gaming and artificial intelligence, coined the term “Machine Learning ” in 1959 while at IBM. He defined machine learning as “the field of study that gives computers the ability to learn without being explicitly programmed “. However, there is no universally accepted definition for machine learning. Different authors define the term differently. We give below two more definitions.

Machine learning is programming computers to optimize a performance criterion using example data or past experience . We have a model defined up to some parameters, and learning is the execution of a computer program to optimize the parameters of the model using the training data or past experience. The model may be predictive to make predictions in the future, or descriptive to gain knowledge from data.
The field of study known as machine learning is concerned with the question of how to construct computer programs that automatically improve with experience.
Definition of learning: 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 T, as measured by P , improves with experience E.

Examples

Handwriting recognition learning problem
Task T : Recognizing and classifying handwritten words within images
Performance P : Percent of words correctly classified
Training experience E : A dataset of handwritten words with given classifications
A robot driving learning problem
Task T : Driving on highways using vision sensors
Performance P : Average distance traveled before an error
Training experience E : A sequence of images and steering commands recorded while observing a human driver
Definition: A computer program which learns from experience is called a machine learning program or simply a learning program .

Classification of Machine Learning
Machine learning implementations are classified into four major categories, depending on the nature of the learning “signal” or “response” available to a learning system which are as follows:

A. Supervised learning:
Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs. The given data is labeled . Both classification and regression problems are supervised learning problems .

Example — Consider the following data regarding patients entering a clinic . The data consists of the gender and age of the patients and each patient is labeled as “healthy” or “sick”.