Learning

Definition of learning:

Definition:

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

 

Examples:

i)  Handwriting recognition learning problem

•  Task T: Recognising and classifying handwritten words within images

•  Performance P: Percent of words correctly classified

•  Training experience E: A dataset of handwritten words with given classifications

ii)  A robot driving learning problem

•  Task T: Driving on highways using vision sensors

•  Performance measure P: Average distance traveled before an error

•  training experience: A sequence of images and steering commands recorded while observing a human driver

iii)  A chess learning problem

•  Task T: Playing chess

•  Performance measure P: Percent of games won against opponents

•  Training experience E: Playing practice games against itself

Definition

A computer program which learns from experience is called a machine learning program or simply a learning program. Such a program is sometimes also referred to as a learner.

 

Components of Learning

Basic components of learning process

The learning process, whether by a human or a machine, can be divided into four components, namely, data storage, abstraction, generalization and evaluation. 

Figure 1.1 illustrates the various components and the steps involved in the learning process.

1.  Data storage

Facilities for storing and retrieving huge amounts of data are an important component of the learning process. Humans and computers alike utilize data storage as a foundation for advanced reasoning.

•  In a human being, the data is stored in the brain and data is retrieved using electrochemical  signals.

•  Computers use hard disk drives, flash memory, random access memory and similar devices to store data and use cables and other technology to retrieve data.

 

2.  Abstraction

The second component of the learning process is known as abstraction.

Abstraction is the process of extracting knowledge about stored data. This involves creating general concepts about the data as a whole. The creation of knowledge involves application of known models and creation of new models.

The process of fitting a model to a dataset is known as training. When the model has been trained, the data is transformed into an abstract form that summarizes the original information.

3.  Generalization

The third component of the learning process is known as generalisation.

The term generalization describes the process of turning the knowledge about stored data into a form that can be utilized for future action. These actions are to be carried out on tasks that are similar, but not identical, to those what have been seen before. In generalization, the goal is to discover those properties of the data that will be most relevant to future tasks.

4.  Evaluation

Evaluation is the last component of the learning process.

It is the process of giving feedback to the user to measure the utility of the learned knowledge. This feedback is then utilised to effect improvements in the whole learning process