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Brief Introduction
Belong to an area of artificial intelligence concerned with the development of techniques and methodologies which allow computers to "learn" a particular skill, Machine Learning is also nothing much than an ideology of creating computer programs through the analysis of data sets. The fundamental of Machine Learning depends heavily on statistics, particularly with Bayesian inference of probability and statistics. Although both fields study the analysis of data, unlike statistics, Machine Learning is concerned with algorithmic complexity of computational implementations.
Machine learning has a wide spectrum of applications including search engines, medical diagnosis, detecting credit card fraud, stock market analysis, classifying DNA sequences, speech and handwriting recognition, object recognition in computer vision, game playing and robot locomotion.
Bayesian Statistics
Some Machine Learning systems attempt to eliminate the need for human intuition in the analysis of the data, while others adopt a collaborative approach between human and machine, in some areas also known as Human-Computer Interactions (HCI). Human intuition cannot be entirely eliminated since the designer of the system must specify how the data are to be represented and what mechanisms will be used to search for a characterisation of the data. Furthermore, Machine Learning can be viewed as an attempt to automate parts of the scientific method. In some research areas, methods are created within the framework of Bayesian statistics, such as Gaussian process prior models.
