Machine Learning Introduction

1.1 Introduction
Machine learning is programming computers to optimize a performance criterion using example data or past experience.
Learning is used when:
Human expertise does not exist (navigating on Mars),
Humans are unable to explain their expertise (speech recognition)
Solution changes in time (routing on a computer network)
Solution needs to be adapted to particular cases (user biometrics)

To solve a problem on a computer, we need an algorithm. An algorithm is a sequence of instructions that should be carried out to transform the input to output. For example, one can devise an algorithm for sorting. The input is a set of numbers and the output is their ordered list. For the same task, there may be various algorithms and we may be interested in finding the most efficient one, requiring the least number of instructions or memory or both.
Application of machine learning methods to large databases is called data mining. In data mining, a large volume of data is processed to construct a simple model with valuable use, for example, having high predictive accuracy. Its application areas are abundant: In addition to retail, in finance banks analyze their past data to build models to use in credit applications, fraud detection, and the stock market. In manufacturing, learning models are used for optimization, control, and troubleshooting. In medicine, learning programs are used for medical diagnosis. In telecommunications, call patterns are analyzed for network optimization and maximizing the quality of service.