What is Machine Learning
What is Machine Learning
•
“Machine Learning
•
enables a Machine to Automatically learn
from Data,
•
improve performance from an Experience
and
•
predict things without explicitly
programmed.”
•
In Simple Words, when we fed the Training
Data to Machine Learning Algorithm,
ü this algorithm will produce a mathematical model andü with the help of the mathematical model,ü the machine will make a prediction andü take a decision without being explicitly programmed.
· Also,
during training data,
o
the more machine will work with
it
o
the more it will get experience and
o
the more efficient result is produced.
Example:
In Driverless Car,
the training data is fed to an Algorithm like
ü how
to Drive a Car on Highway, Busy and Narrow Street with
factors like
· speed
limit,
· parking,
· stop
at signals etc.
After that, a Logical
and Mathematical model is created on the basis of that and
after that, the car will work according to the logical model.
Also, the more data, the
data is fed the more efficient output is produced.
Definition: A computer program is said to
ü 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.
To have a well-defined learning problem, three
features needs to be identified:
- The class of tasks
- The measure of performance to
be improved
- The source of experience
Simple learning process
•
For any learning system, we must be
knowing the three elements —
ü T
(Task),
ü P
(Performance Measure),
ü E
(Training Experience).
•
At a high level,
the process of learning
system looks like below fig.
The learning
process starts with
ü task T,
ü performance measure
P and
ü training experience
E and
ü objective are to
find an unknown target function.
The target
function is
ü an exact
knowledge to be learned from the training experience and
ü its
unknown.
For example, in a case of credit
approval,
•
the learning system will
have
ü customer
application records as experience and
ü task would be
to classify whether the given customer application is eligible
for a loan.
So in this case,
ü the training examples can be represented as
ü where
•
x represents customer
application details and
•
y represents the status of
credit approval.
•
With these
details, what is that exact knowledge to be learned from the training
experience?
•
So the target
function to be learned in the credit approval learning system is a mapping
function f:x →y.
•
This function
represents the exact knowledge defining the relationship
between input variable x and output variable y.
•
The learning
algorithms try to guess a “hypothesis’’ function h(X) that approximates
the unknown f(.).
•
A hypothesis is a function that best
describes the target and Hypothesis set or space H(.) is
the collection of all the possible legal hypothesis.
•
This is the
set from which the machine learning algorithm would determine the
best possible (only one) which would best describe the target
function or the outputs.
•
The goal of
the learning process is to find the final hypothesis that best approximates the
unknown target function.
Examples
i. Checkers game: A computer program that learns to play checkers might
improve its performance as measured by its ability to
win at the class of tasks involving playing checkers games, through
experience obtained by playing games against itself.
A checkers learning problem:
- Task
T: playing
checkers
- Performance
measure P: percent
of games won against opponents
- Training
experience E: playing
practice games against itself
ii. A handwriting recognition learning problem:
- Task T: recognizing and classifying handwritten words within images
- Performance measure P:
percent of words correctly classified
- Training experience E: a database of handwritten words
with given
classifications
iii. A robot
driving learning problem:
· Task T: driving on public four-lane highways using vision sensors
· Performance
measure P: average distance travelled before an error (as judged by human overseer)
· Training experience E: a sequence of images
and steering commands recorded
while observing a human driver
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