An Introduction to Computational Learning Theory (The MIT Press) by Michael J. Kearns (PDF)

Description

Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics.Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics. Computational learning theory is a new and rapidly expanding area of research that examines formal models of induction with the goals of discovering the common methods underlying efficient learning algorithms and identifying the computational impediments to learning. Each topic in the book has been chosen to elucidate a general principle, which is explored in a precise formal setting. Intuition has been emphasized in the presentation to make the material accessible to the nontheoretician while still providing precise arguments for the specialist. This balance is the result of new proofs of established theorems, and new presentations of the standard proofs. The topics covered include the motivation, definitions, and fundamental results, both positive and negative, for the widely studied L. G. Valiant model of Probably Approximately Correct Learning; Occam’s Razor, which formalizes a relationship between learning and data compression; the Vapnik-Chervonenkis dimension; the equivalence of weak and strong learning; efficient learning in the presence of noise by the method of statistical queries; relationships between learning and cryptography, and the resulting computational limitations on efficient learning; reducibility between learning problems; and algorithms for learning finite automata from active experimentation.

User’s Reviews

Editorial Reviews: About the Author Michael J. Kearns is Professor of Computer and Information Science at the University of Pennsylvania.Umesh Vazirani is Roger A. Strauch Professor in the Electrical Engineering and Computer Sciences Department at the University of California, Berkeley.

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐…about machine learning since learning algorithms are, in fact, algorithms. At a high level, computational learning theory answers the same sort of questions as statistical learning theory (“What kind of guarantees can I make about my learning procedure? In what situations is learning possible?”) with different tools and methodology. Trade in your operator equations, modes of convergence, and support vectors for boolean formulae, complexity classes, and quadratic residues, but don’t worry; the trade is temporary, since the theories are complementary, and short-lived, since the book is easy and quick to read. At well under 200 large-type pages, you can mow through it armed with little besides Big-O notation, basic probability, and a few (IID) samples of your favorite stimulant.In return for your mild effort, you will be acquainted with the PAC model of learning and techniques for reasoning about tractability, sample size, connections to well-known problems, etc. The best material, in my opinion, relates to the importance of problem representation and methods for establishing the difficulty of efficient predictability. Even the most unsatisfying material (the treatment of Occam’s razor and the description of VC dimension) isn’t stale, and wasn’t really bad to start; this, despite the book’s age (15 years in a 25 year old subfield), is most probably* a testament to the book’s value as an approachable introduction.* (As usual, some positive probability is reserved to indict the field’s lack of advancement. But not much).

⭐The few chapters I have read of this book seem good. Good examples which is nice.

⭐

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