Machine learning


What is machine learning?

A computer program is said to learn from experience E concerning some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.
The three features of well-defined learning problem
  • The class of tasks.
  • The measure of performance to be improved.
  • The source of experience.

A checkers learning problem:
  • Task T: playing checkers.
  • Performance measure P: percent of games won against opponents.
  • Training experience E: playing practice games against itself.

A handwriting recognition learning problem:
  • Task T: recognizing and classifying handwritten words within images.
  • Performance measure P: percent of words correctly classified.
  • Training experience E: a database of handwritten words with given classifications.

A robot driving learning problem:
  • Task T: driving on public four-lane highways using vision sensors.
  • Performance measure P: average distance traveled before an error (as judged by human overseer).
  • Training experience E: a sequence of images and steering commands recorded while observing a human driver.

Why machine learning?

Recent progress in algorithms and theory. The growing flood of online data. Computational power is available. Budding industry. 

What is machine learning?

  • A computer program is said to learn from experience E concerning some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.
    The three features of well-defined learning problem
    • The class of tasks.
    • The measure of performance to be improved.
    • The source of experience.


    A checkers learning problem:
    • Task T: playing checkers.
    • Performance measure P: percent of games won against opponents.
    • Training experience E: playing practice games against itself.


    A handwriting recognition learning problem:
    • Task T: recognizing and classifying handwritten words within images.
    • Performance measure P: percent of words correctly classified.
    • Training experience E: a database of handwritten words with given classifications.

    A robot driving learning problem:
    • Task T: driving on public four-lane highways using vision sensors.
    • Performance measure P: average distance traveled before an error (as judged by human overseer).
    • Training experience E: a sequence of images and steering commands recorded while observing a human driver.