UNIT IV

LEARNING

Introduction to Learning

Machine Learning is the study of how to build computer systems that adapt and improve

with experience. It is a subfield of Artificial Intelligence and intersects with cognitive

science, information theory, and probability theory, among others.

Classical AI deals mainly with deductive reasoning, learning represents inductive

reasoning. Deductive reasoning arrives at answers to queries relating to a particular

situation starting from a set of general axioms, whereas inductive reasoning arrives at

general axioms from a set of particular instances.

Classical AI often suffers from the knowledge acquisition problem in real life

applications where obtaining and updating the knowledge base is costly and prone to

errors. Machine learning serves to solve the knowledge acquisition bottleneck by

obtaining the result from data by induction.

Machine learning is particularly attractive in several real life problem because of the

following reasons:

• Some tasks cannot be defined well except by example

• Working environment of machines may not be known at design time

• Explicit knowledge encoding may be difficult and not available

• Environments change over time

• Biological systems learn

Recently, learning is widely used in a number of application areas including,

• Data mining and knowledge discovery

• Speech/image/video (pattern) recognition

• Adaptive control

• Autonomous vehicles/robots

• Decision support systems

• Bioinformatics

• WWW

Formally, 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.

Thus a learning system is characterized by:

• task T

• experience E, and

• performance measure P