Machine Learning and Neural Network: An Introduction to Beginners

Machine
Learning and Neural Network: An Introduction to Beginners
Prof. (Dr.) S. Shyam Prasad
Introduction
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 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
T
= the task of playing chess.
P
= the probability that the program will win the next game.
Broadly,
machine learning problems can be classified as: Supervised learning and
Unsupervised learning.
Supervised
Learning
In
supervised learning, we know both the input variable say ‘x’ and the correct
output say ‘y’ and we also have the knowledge that there is a relationship
between the input and the output. These problems can be of two types:
regression and classification problems. In a regression problem, we try to
predict results within a continuous output. In other words, we try to map input
variables to some continuous function. Instead, in a classification problem, we
try to predict results in a discrete output. In other words, we try to map
input variables into discrete categories.
Unsupervised
Learning
In
unsupervised learning, we have the input variable say ‘x’ but have little or no
idea about correct output. All that we do is derive a structure from that data
where we don’t necessarily know the effect of the variables.  This is done by clustering the data based on
relationships among the variables in the data.
Further
explanation or discussion would require mathematics and so we dwell no further
in this direction. Readers interested in the topic can choose a simple course
from MOOC for further reading. However, before moving, let us understand the
reasons for machine learning being so prevalent today. In fact, machine
learning is a field that has grown out of the field of artificial intelligence,
commonly known as AI. In the field of AI, the scientist while attempting to
build intelligent machines found that they can programme a machine to do some
basic things such as how to find the shortest path from A to B but did not know
how to write AI programmes to do more interesting and complex things.
Scientists realized that the only way to do these things was to have the machine
learn to do it by itself. So, machine learning was developed as a new
capability for computers and today it touches many segments of industry
and basic science. Range of problems that machine learning touches is
far and wide such as robotics, computational biology and tons of things in
Silicon Valley. For example, when every time one goes to Amazon or Netflix or
iTunes Genius, it recommends the movies or products and music to buy and that’s
machine learning algorithm at work. However, there are about million users; one
cannot write million different programmes for million users. The only solution
that one can think of is that the software has to learn by itself to customize
the preferences.
In
a slightly dated article accessed over internet that listed top IT skills, at the
top of this list of the twelve most desirable IT skills was machine
learning. At least in US, a number of recruiters contact universities and
enquire about graduating machine learning students.  Apparently, there seems to exist a vast,
unfulfilled demand for this skill set, and this is a great time to learn
about machine learning.
Neural Networks
Neural
Network is also a machine learning algorithm. Neutral network was
actually an old idea, but had fallen out of favor for a
while. But today, it is the state of the art technique for many
different machine learning problems. Neural Networks are actually very
effective state of the art technique for modern day machine learning
applications and get them to work well on problems.
Conclusion
Machine
learning is a method of data analysis.  The specialty of machine learning algorithm is
that on being used repeatedly it learns from data.  It allows computers to find hidden insights
without being explicitly programmed as where to look. As mentioned earlier, growing
volumes and varieties of available data, increasing power of computation and
reducing cost of computation and more and cheaper data storage facilities have
all made machine learning very popular.
Machine
learning is now used in market segmentation, predicting consumer behaviour,
employees’ growth and career more accurately, segregating more potential patients
for certain diseases and many more. It emerges from above that the scope of
machine learning seems to be unlimited.

Armed
with above knowledge, one is in a position to understand the terms machine
learning and Neural Network. In general, there is wide scope for people with good
knowledge of mathematics and particularly IT and electrical engineers to cash
in on this opportunity. Management knowledge would be an added advantage. 
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