INTRODUCTION MACHINE LEARNING
I hope that future versions will cover Hop eld nets, Elman nets and other re-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.
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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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