INTRODUCTION MACHINE LEARNING
current nets, radial basis functions, grammar and automata learning, genetic algorithms, and Bayes networks :::. I am also collecting exercises and project suggestions which will appear in future versions. My intention is to pursue a middle ground between a theoretical textbook and one that focusses on applications. The book concentrates on the ...
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INTRODUCTION MACHINE LEARNING - Stanford AI Lab
ai.stanford.edu1.1 Introduction 1.1.1 What is Machine Learning? Learning, like intelligence, covers such a broad range of processes that it is dif- cult to de ne precisely. A dictionary de nition includes phrases such as \to gain knowledge, or understanding of, or skill in, by study, instruction, or expe-
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