2. MACHINE LEARNING - INTRODUCTION

 DEFINITION:

Machine Learning is a set of methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data or to perform other kinds of decision making under uncertainty (such as planning how to collect more data).

LEARNING PROBLEM:

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 in T, as measured by P, improves with experience E.

Example: a computer program that learns to play checkers P in this case is measured by its ability to win

T in this case is playing checkers games

E in this case is obtained by playing games against itself

In general, to have a well-defined learning problem we must identify these three features:

1.         The class of tasks (T)

2.         The measure of performance to be improved (P)

3.         The source of experience (E)

Checkers learning problem:

·       Task T – playing checkers

·       Performance measure P – percent of games won against opponents

·       Training experience E – playing practice games against itself

 

Handwriting recognition learning problem:

·       Task T – recognizing and classifying handwritten words within images

·       Performance measure P – percentage of words correctly classified

·       Training Experience E – a database of handwritten words with given classifications

Robot driving learning problem:

·       Task T – driving on public four lane highways using vision sensors

·       Performance measure P – average distance travelled before an error (as judged by human overseer)

·       Training experience E – a sequence of images and steering commands recorded while observing a human driver

E-mail classification:

·       Task T: Categorize email messages as spam or legitimate.

·       Performance P: Percentage of email messages correctly classified.

·       Training Experience E: Database of emails, some with human-given labels