Machine Learning and Pattern Recognition

Introduction

In very simple language, Pattern Recognition is a type of problem while Machine Learning is a type of solution. Pattern recognition is closely related to Artificial Intelligence and Machine Learning. Pattern Recognition is an engineering application of Machine Learning. Machine Learning deals with the construction and study of systems that can learn from data, rather than follow only explicitly programmed instructions whereas Pattern recognition is the recognition of patterns and regularities in data.

1. Machine Learning

The goal of Machine Learning is never to make "perfect" guesses because Machine Learning deals in domains where there is no such thing. The goal is to make guesses that are good enough to be useful. Machine Learning is a method of data analysis that automates analytical model building. Machine Learning is a field that uses algorithms to learn from data and make predictions. A Machine Learning algorithm then takes these examples and produces a program that does the job. Machine Learning builds heavily on statistics. For example, when we train our machine to learn, we have to give it a statistically significant random sample as training data. If the training set is not random, we run the risk of the Machine Learning patterns that aren’t actually there.

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