#ISMEianWrites
Machine Learning and Neural Network: An Introduction to
Beginners
(Dr.)
Prof. Prof.
(Dr.) S.
Shyam
S SHYAM
PRASAD,
Prasad
Professor – Marketing,
ISME
machine learning. An earlier definition by Arthur
Samuel described machine learning as: “the field
of study that gives computers the ability to learn
without being explicitly programmed.”
Machine Learning
In the present era of big data and hypercompetition
a plethora of tools is being used to predict
the future as accurately as possible to gain
competitive advantage. One often comes across
subjects such as Machine Learning and Neural
Network amongst many other. The intention of
this small write up is to introduce the reader
to these terms and particularly the students
to make them aware so that they may prepare
themselves for deeper study or a career in them
if they find it interesting. The institutes can also
think of introducing short-term courses in these
subjects.
Machine Learning
We have been using machine learning many
times a day without realizing it. A simple example
would be Google search. Every time we do a
Google search, the search engine works so well
because Google’s machine learning software
guesses our search intention and accordingly
ranks the pages. Even the email spam filter
that separates out the chaff and saves us the
time and effort in handling emails is an example
of machine learning. At a higher level, getting
robots to drive a car or tidy up the house are
also examples of machine learning. Scientists
are hopeful that progress in this direction can be
made through learning algorithms called neural
networks. Neural networks imitate our brain
and resemble its working. Discussion on neural
networks is done later in this write up.
On going through the literature of machine
learning, one comes across two definitions of
We have been using machine learning many
times a day without realizing it. A simple example
would be Google search. Every time we do a
Google search, the search engine works so well
because Google’s machine learning software
guesses our search intention and accordingly
ranks the pages. Even the email spam filter that
separates out the chaff and saves us the time
and effort in handling emails is an example of
machine learning. At a higher level, getting
robots to drive a car or tidy up the house are
also examples of machine learning. Scientists
are hopeful that progress in this direction can be
made through learning algorithms called neural
networks. Neural networks imitate our brain
and resemble its working. Discussion on neural
networks is done later in this write up.
On going through the literature of machine
learning, one comes across two definitions of
machine learning. An earlier definition by Arthur
Samuel described machine learning as: “the field
of study that gives computers the ability to learn
without being explicitly programmed.”
A formal and a modern definition is given by Tom
Mitchell and the definition is: “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: playing chess.
E = the experience of playing many games of
chess