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.