Machine learning – history and definition
It is difficult to give an exact history, but the definition of machine learning we use today finds its usage as early as the 1860s. In Rene Descartes' Discourse on the Method, he refers to Automata and says:
For we can easily understand a machine's being constituted so that it can utter words, and even emit some responses to action on it of a corporeal kind, which brings about a change in its organs; for instance, if touched in a particular part it may ask what we wish to say to it; if in another part it may exclaim that it is being hurt, and so on.
Alan Turing, in his famous publication Computing Machinery and Intelligence gives basic insights into the goals of machine learning by asking the question "Can machines think?".
Arthur Samuel in 1959 wrote, "Machine Learning is the field of study that gives computers the ability to learn without being explicitly programmed.".
Tom Mitchell in recent times gave a more exact definition of machine learning: "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."
Machine learning has a relationship with several areas:
- Statistics: It uses the elements of data sampling, estimation, hypothesis testing, learning theory, and statistical-based modeling, to name a few
- Algorithms and computation: It uses the basic concepts of search, traversal, parallelization, distributed computing, and so on from basic computer science
- Database and knowledge discovery: For its ability to store, retrieve, and access information in various formats
- Pattern recognition: For its ability to find interesting patterns from the data to explore, visualize, and predict
- Artificial intelligence: Though it is considered a branch of artificial intelligence, it also has relationships with other branches, such as heuristics, optimization, evolutionary computing, and so on
