Overview on machine learning

What is machine learning

As the name implies machine learning means making the computer able to learn on
its own. It is one of the most active research area within the purview of artificial intelligence.

Technically it is defined as computational methods using the past information available to improve performance with practice and to acquire knowledge automatically.

Machine learning involves designing efficient and accurate prediction algorithms. It uses induction as a way of thinking. In fact it relies on induction process to a great extent. The algorithm designed for this purpose gets labelled training examples and produces the result in the form of prediction rule.

As the complexity, variety and size of machine learning models increases it is required to make use of optimization approaches having great availability and theoretical properties.

Need for machine learning

Learning is a central feature in intelligent systems. It is not possible to build an intelligent system without having a learning module. It also contributes to developing mechanisms for cognition, perception and action.

Types of machine learning

There are two kinds of learning: Supervised and unsupervised. Supervised approach aims to deduct input-output relations based on input-output samples. Once this relation is learned, it is easy to predict output values for unknown input points. Unsupervised approach on the other hand does not accept output values as training samples. It actually depends on the situation and aims to extract relevant information from the given data.

Goals

The main goal is to design general purpose algorithms. These algorithms should be efficient and should consider amount of data required. Moreover they should be applicable to wide variety of problems.

The result of machine learning process should be a prediction rule that makes predictions as accurate as possible. Human experts should be able to understand these predictions easily.

Applications

Applications of machine learning include classifying text, language processing, speech recognition, computational biology, games, diagnosis in medical field, computer vision, data mining, robot control and so on.