Arthur Samuel coined the term machine learning in 1959 while at IBM. A popular definition of machine learning is due to Arthur, who, it is believed, called machine learning a field of computer science that gives computers the ability to learn without being explicitly programmed.
A simple explanation would help to illustrate this concept. By now, most of us are familiar with the concept of spam in emails. Most email accounts also contain a separate folder known as Junk, Spam, or a related term. A cursory check of the folders will usually indicate the presence of several emails, many of which were presumably unsolicited and contain meaningless information.
The mere task of categorizing emails as spam and moving them to a folder involves the application of machine learning. Andrew Ng highlighted this elegantly in his popular MOOC course on machine learning.
In Mitchell's terms, the spam classification process involves:
- Task T: Classifying emails as spam/not spam
- Performance P: Number of emails accurately identified as spam
- Experience E: The model is provided emails that have been marked as spam/not spam and uses that information to determine whether a new email is spam or not
Broadly speaking, there are two distinct types of machine learning:
- Supervised machine learning
- Unsupervised machine learning
We shall discuss them in turn here.