Introduction to Machine Learning Lecture notes

CSE176 Introduction to Machine Learning Lecture notes Miguel A . Carreira-Perpin a n EECS, University of California, Merced November 28, 2016. These are notes for a one-semester undergraduate course on Machine Learning given by Prof. Miguel A . Carreira-Perpin a n at the University of California, Merced. The notes are largely based on the book Introduction to Machine Learning by Ethem Alpayd n (MIT Press, 3rd ed., 2014), with some additions. These notes may be used for educational, non-commercial purposes. c 2015 2016 Miguel A . Carreira-Perpin a n 1 Introduction What is Machine Learning (ML)? Data is being produced and stored continuously ( big data ): science: genomics, astronomy, materials science, particle accelerators.. sensor networks: weather measurements, traffic.. people: social networks, blogs, mobile phones, purchases, bank transactions.. etc. Data is not random; it contains structure that can be used to predict outcomes, or gain knowl- edge in some way.

2.6 Regression •Training set X= {(xn,yn)}N n=1 where the label for a pattern xn ∈R D is a real value y n ∈R. In multivariate regression, yn ∈Rd is a real vector. •We assume the labels were produced by applying an unknown function fto the instances, and we want to learn (or estimate) that function using functions hfrom a hypothesis ...

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