Well Posed Learning Problems

Unit - 1
Introduction: Well Posed Learning Problems

Q.1) Define learning.

Ans: Learning is a phenomenon and process which has manifestations of various aspects. Learning process includes gaining of new symbolic knowledge and development of cognitive skills through instruction and practice. It is also discovery of new facts and theories through observation and experiment.

Q.2)  Define machine learning.

Ans.: A computer program is said to leam 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

Q.3) What is need of machine learning in this era?

Ans.: Main goal of machine learning is to devise learning algorithms that do the learning automatically without human intervention or assistance.

• The machine learning paradigm can be viewed as "programming by example".  Another goal is to develop computational models of human learning process and perform computer simulations.
• The goal of machine learning is to build computer systems that can adapt and learn from their experience.
• Machine learning algorithms can figure out how to perform important tasks by generalizing from examples
• Machine learning provides business insight and intelligence. Decision makers are provided with greater insights into their organizations. This adaptive technology is being used by global enterprises to gain a competitive edge.
• Machine learning algorithms discover the telationships between the variables of a system (input, output and hidden) from direct samples of the system.

Q.4) What are T, P, E ? How do we formulate a machine learning problem ?

Ans.: In general, to have a well-defined learning problem, we must identity these three features : the class of tasks, the measure of performance to be improved, and the source of experience.

A Robot Driving Learning Problem

1. Task T : Driving on public, 4-lane highway using vision sensors.
2. Performance measure P : Average distance traveled before an error (as judged by human overseer).
3. Training experience E : A sequence of images and steering commands recorded while observing a human driver.

A Handwriting Recognition Learning Problem

1. Task = T : Recognizing and classifying handwritten words within images
2. Performance measure P: Percent of words correctly classified.
3. Training experience E: A database of handwritten words with given classifications.

Text Categorization Problem

1. Task T: Assign a document to its content category.
2. Performance measure P: Precision and Recall.
3. Training experience E: Example pre-classified documents.

JNTUH R18 MACHINE LEARNING NOTES UNIT 1