The Book of Why

The Book of Why
By:Judea Pearl,Dana Mackenzie
Published on 2018-05-15 by Hachette UK

A Turing Award-winning computer scientist and statistician shows how understanding causality has revolutionized science and will revolutionize artificial intelligence |Correlation is not causation.| This mantra, chanted by scientists for more than a century, has led to a virtual prohibition on causal talk. Today, that taboo is dead. The causal revolution, instigated by Judea Pearl and his colleagues, has cut through a century of confusion and established causality--the study of cause and effect--on a firm scientific basis. His work explains how we can know easy things, like whether it was rain or a sprinkler that made a sidewalk wet; and how to answer hard questions, like whether a drug cured an illness. Pearl's work enables us to know not just whether one thing causes another: it lets us explore the world that is and the worlds that could have been. It shows us the essence of human thought and key to artificial intelligence. Anyone who wants to understand either needs The Book of Why.

This Book was ranked at 11 by Google Books for keyword Math book.

Book ID of The Book of Why's Books is BzM0DwAAQBAJ, Book which was written byJudea Pearl,Dana Mackenziehave ETAG "I65tQLbdRPk"

Book which was published by Hachette UK since 2018-05-15 have ISBNs, ISBN 13 Code is 9780465097616 and ISBN 10 Code is 0465097618

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Descriptive Complexity, Canonisation, and Definable Graph Structure Theory

Descriptive Complexity, Canonisation, and Definable Graph Structure Theory
By:Martin Grohe
Published on 2017-07-31 by Cambridge University Press

Descriptive complexity theory establishes a connection between the computational complexity of algorithmic problems (the computational resources required to solve the problems) and their descriptive complexity (the language resources required to describe the problems). This groundbreaking book approaches descriptive complexity from the angle of modern structural graph theory, specifically graph minor theory. It develops a 'definable structure theory' concerned with the logical definability of graph theoretic concepts such as tree decompositions and embeddings. The first part starts with an introduction to the background, from logic, complexity, and graph theory, and develops the theory up to first applications in descriptive complexity theory and graph isomorphism testing. It may serve as the basis for a graduate-level course. The second part is more advanced and mainly devoted to the proof of a single, previously unpublished theorem: properties of graphs with excluded minors are decidable in polynomial time if, and only if, they are definable in fixed-point logic with counting.

This Book was ranked at 28 by Google Books for keyword Math book.

Book ID of Descriptive Complexity, Canonisation, and Definable Graph Structure Theory's Books is RLYrDwAAQBAJ, Book which was written byMartin Grohehave ETAG "B0Ag0Hmkz68"

Book which was published by Cambridge University Press since 2017-07-31 have ISBNs, ISBN 13 Code is 9781107014527 and ISBN 10 Code is 1107014522

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Mathematics for Machine Learning

Mathematics for Machine Learning
By:Marc Peter Deisenroth,A. Aldo Faisal,Cheng Soon Ong
Published on 2020-04-23 by Cambridge University Press

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.

This Book was ranked at 3 by Google Books for keyword Math book.

Book ID of Mathematics for Machine Learning's Books is t4XQDwAAQBAJ, Book which was written byMarc Peter Deisenroth,A. Aldo Faisal,Cheng Soon Onghave ETAG "H8ex0YIc0Yc"

Book which was published by Cambridge University Press since 2020-04-23 have ISBNs, ISBN 13 Code is 9781108569323 and ISBN 10 Code is 1108569323

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