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Machine learning
Machine learning is a branch of science that deals
with programming the systems in such a way that they automatically learn and
improve with experience. Here, learning means recognizing and understanding the
input data and making wise decisions based on the supplied data.
It
is very difficult to receive all the decisions based on all possible inputs. To
tackle this problem, algorithms are developed. These algorithms build knowledge
from specific data and past experience with the principles of statistics,
probability theory, logic, combinatorial optimization, search, reinforcement
learning, and control theory.
The developed algorithms form the
basis of various applications such as: Vision processing, Language processing, Forecasting
(e.g., stock market trends), Pattern recognition, Games, Data mining, Expert
systems, Robotics
There are
several ways to implement machine learning techniques, however the most
commonly used ones are supervised and unsupervised learning.
In
supervised systems, the data as presented to a machine learning algorithm is
fully labelled. Classifier is learned from the data, the process of assigning
labels to yet unseen instances is called classifi- cation. All examples are
presented with a classification that the machine is meant to reproduce.
Unsupervised
systems are not provided any training examples at all and conduct clustering.
This is the division of data instances into several groups. The results of
clustering algorithms are data driven, hence more ’natural’ and better suited
to the underlying structure of the data.
