Machine learning is the subfield of computer science that, according to Arthur Samuel in 1959, gives "computers the ability to learn without being explicitly programmed." - https://en.wikipedia.org/wiki/Machine_learning

Terminology and concepts

  • Supervised machine learning: The program is "trained" on a pre-defined set of "training examples", which then facilitate its ability to reach an accurate conclusion when given new data.
  • Unsupervised machine learning: The program is given a bunch of data and must find patterns and relationships therein.
  • "The goal of ML is never to make 'perfect' guesses, because ML deals in domains where there is no such thing. The goal is to make guesses that are good enough to be useful."
  • Machine learning builds heavily on statistics.

Prerequisites

  • Statistics
  • Linear Algebra
  • Calculus

Resources

See Also