Link To Content
Learning, like intelligence, covers such a broad range of processes that it is difficult to define precisely. A dictionary definition includes phrases such as “to gain knowledge, or understanding of, or skill in, by study, instruction, or experience,” and “modification of a behavioral tendency by experience.” Zoologists and psychologists study learning in animals and humans. In this book we focus on learning in machines. There are several parallels between animal and machine learning. Certainly, many techniques in machine learning derive from the efforts of psychologists to make more precise their theories of animal and human learning through computational models. It seems likely also that the concepts and techniques being explored by researchers in machine learning may illuminate certain aspects of biological learning.
My intention is to pursue a middle ground between a theoretical text book and one that focuses on applications. The book concentrates on the important ideas in machine learning. I do not give proofs of many of the theorems that I state, but I do give plausibility arguments and citations to formal proofs. And, I do not treat many matters that would be of practical importance in applications; the book is not a handbook of machine learning practice. Instead, my goal is to give the reader sufficient preparation to make the extensive literature on machine learning accessible.
Learning, like intelligence, covers such a broad range of processes that it is difficult to define precisely. A dictionary definition includes phrases such as “to gain knowledge, or understanding of, or skill in, by study, instruction, or experience,” and “modification of a behavioral tendency by experience.” Zoologists and psychologists study learning in animals and humans. In this book we focus on learning in machines. There are several parallels between animal and machine learning. Certainly, many techniques in machine learning derive from the efforts of psychologists to make more precise their theories of animal and human learning through computational models. It seems likely also that the concepts and techniques being explored by researchers in machine learning may illuminate certain aspects of biological learning.
My intention is to pursue a middle ground between a theoretical text book and one that focuses on applications. The book concentrates on the important ideas in machine learning. I do not give proofs of many of the theorems that I state, but I do give plausibility arguments and citations to formal proofs. And, I do not treat many matters that would be of practical importance in applications; the book is not a handbook of machine learning practice. Instead, my goal is to give the reader sufficient preparation to make the extensive literature on machine learning accessible.
