Machine Learning: An artificial intelligence methodology
Keywords— machine learning, supervised learning, unsupervised
- Learning is considered as a parameter for
intelligent machines. Deep understanding would
help in taking decisions in a more optimized form
and also help then to work in most efficient method.
As seeing is intelligence, so learning is also
becoming a key to the study of biological and
artificial vision. Instead of building heavy machines
with explicit programming now different algorithms
are being introduce which will help the machine to
understand the virtual environment and based on
their understanding the machine will take particular
decision. This will eventually decrease the number
of programming concepts and also machine will
become independent and take decisions on their own
- Different algorithms are introduced for different
types of machines and the decisions taken by them.
Designing the algorithm and using it in most
appropriate way is the real challenge for the
developers and scientists.
Pattern recognizing is also a concept in machine
learning. Most algorithms use the concept of pattern
recognition to make optimized decisions. As a
consequence of this new interest in learning we are
experiencing a new era in statistical and functional
approximation techniques and their applications to
domain such as computer visions.
Supervised learning is an algorithm in which both
the inputs and outputs can be perceived. Based on
this training data, the algorithm has to generalize
such that it is able to correctly respond to all possible
inputs. This algorithm is expected to produce correct
output for inputs that weren’t encountered during
training. In supervised learning what has to be
learned is specified for each example. Supervised
classification occurs when a trainer provides the
classification for each example. Supervised learning
of actions occurs when the agent is given immediate
feedback about the value of each action.
In order to solve a give problem using supervised
learning algorithm one has to follow some certain
1) Determine the type of training examples.
2) Gather a training set.
3) Determine the input feature representation of
4) Determine the structure of learning function
& corresponding learning algorithm.
5) Complete the design and run the learning
algorithm on the gather set of data.
6) Evaluate the accuracy of the learned function
also the performance of the learning function should
be measured and then the performance should be
again measured on the set which is different from the