Overview of Machine Learning
TL
TL;DR
This chapter first reviews the fundamental concepts of machine learning such as feature assessment, unsupervised versus supervised learning and types of classification, and introduces some supervised learning methods.
Abstract
The machine learning field, which can be briefly defined as enabling computers make successful predictions using past experiences, has exhibited an impressive development recently with the help of the rapid increase in the storage capacity and processing power of computers. Together with many other disciplines, machine learning methods have been widely employed in bioinformatics. The difficulties and cost of biological analyses have led to the development of sophisticated machine learning approaches for this application area. In this chapter, we first review the fundamental concepts of machine learning such as feature assessment, unsupervised versus supervised learning and types of classification. Then, we point out the main issues of designing machine learning experiments and their performance evaluation. Finally, we introduce some supervised learning methods
References5 items
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The Elements of Statistical Learning
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Kalman filter model of the visual cortex
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Decide on inputs and template for the classifier (feature engineering, model selection)
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Use ML algorithm to fill-in template so resulting classifier is "well-fit
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