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Introduction to Machine Learning - CmpE WEB

Introduction TO Machine Learning 3RD EDITION ETHEM ALPAYDIN The MIT Press, 2014 ~ethem/i2ml3e Lecture Slides for CHAPTER 1: Introduction Big Data 3 Widespread use of personal computers and wireless communication leads to big data We are both producers and consumers of data Data is not random, it has structure, , customer behavior We need big theory to extract that structure from data for (a) Understanding the process (b) Making predictions for the future Why Learn ? 4 Machine Learning is programming computers to optimize a performance criterion using example data or past experience. There is no need to learn to calculate payroll 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) What We Talk About When We Talk About Learning 5 Learning general models from a data of particular examples Data is cheap and abundant (data warehouses, data marts); knowledge is expensive and scarce.

Why “Learn” ? 4 Machine learning is programming computers to optimize a performance criterion using example data or past experience. There is no need to “learn” to calculate payroll Learning is used when:

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