Book Description
In this book, the author presents the results of his thorough and systematic review of the research at the intersection of two apparently rather unrelated fields: Automated Theorem Proving (ATP) and Machine Learning (ML).
Author : Sean B. Holden
Publisher :
Page : 202 pages
File Size : 21,1 MB
Release : 2021-11-22
Category :
ISBN : 9781680838985
In this book, the author presents the results of his thorough and systematic review of the research at the intersection of two apparently rather unrelated fields: Automated Theorem Proving (ATP) and Machine Learning (ML).
Author : Alessandro Armando
Publisher : Springer Science & Business Media
Page : 568 pages
File Size : 40,94 MB
Release : 2008-07-25
Category : Computers
ISBN : 3540710698
methods, description logics and related logics, sati?ability modulo theory, decidable logics, reasoning about programs, and higher-order logics.
Author : Shai Shalev-Shwartz
Publisher : Cambridge University Press
Page : 415 pages
File Size : 26,23 MB
Release : 2014-05-19
Category : Computers
ISBN : 1107057132
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Author : Martin Davis
Publisher :
Page : 40 pages
File Size : 44,75 MB
Release : 1961
Category : Calculus of variations
ISBN :
The programming of a proof procedure is discussed in connection with trial runs and possible improvements. (Author).
Author : Yves Bertot
Publisher : Springer Science & Business Media
Page : 492 pages
File Size : 41,34 MB
Release : 2013-03-14
Category : Mathematics
ISBN : 366207964X
A practical introduction to the development of proofs and certified programs using Coq. An invaluable tool for researchers, students, and engineers interested in formal methods and the development of zero-fault software.
Author : Marc Peter Deisenroth
Publisher : Cambridge University Press
Page : 392 pages
File Size : 28,30 MB
Release : 2020-04-23
Category : Computers
ISBN : 1108569323
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.
Author : David Barber
Publisher : Cambridge University Press
Page : 739 pages
File Size : 42,66 MB
Release : 2012-02-02
Category : Computers
ISBN : 0521518148
A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.
Author : John Harrison
Publisher : Cambridge University Press
Page : 703 pages
File Size : 27,73 MB
Release : 2009-03-12
Category : Computers
ISBN : 0521899575
A one-stop reference, self-contained, with theoretical topics presented in conjunction with implementations for which code is supplied.
Author : Simon Colton
Publisher : Springer Science & Business Media
Page : 384 pages
File Size : 20,23 MB
Release : 2012-12-06
Category : Mathematics
ISBN : 1447101472
In recent years, Artificial Intelligence researchers have largely focused their efforts on solving specific problems, with less emphasis on 'the big picture' - automating large scale tasks which require human-level intelligence to undertake. The subject of this book, automated theory formation in mathematics, is such a large scale task. Automated theory formation requires the invention of new concepts, the calculating of examples, the making of conjectures and the proving of theorems. This book, representing four years of PhD work by Dr. Simon Colton demonstrates how theory formation can be automated. Building on over 20 years of research into constructing an automated mathematician carried out in Professor Alan Bundy's mathematical reasoning group in Edinburgh, Dr. Colton has implemented the HR system as a solution to the problem of forming theories by computer. HR uses various pieces of mathematical software, including automated theorem provers, model generators and databases, to build a theory from the bare minimum of information - the axioms of a domain. The main application of this work has been mathematical discovery, and HR has had many successes. In particular, it has invented 20 new types of number of sufficient interest to be accepted into the Encyclopaedia of Integer Sequences, a repository of over 60,000 sequences contributed by many (human) mathematicians.
Author : Tobias Nipkow
Publisher : Springer
Page : 304 pages
File Size : 44,28 MB
Release : 2014-12-03
Category : Computers
ISBN : 3319105426
Part I of this book is a practical introduction to working with the Isabelle proof assistant. It teaches you how to write functional programs and inductive definitions and how to prove properties about them in Isabelle’s structured proof language. Part II is an introduction to the semantics of imperative languages with an emphasis on applications like compilers and program analysers. The distinguishing feature is that all the mathematics has been formalised in Isabelle and much of it is executable. Part I focusses on the details of proofs in Isabelle; Part II can be read even without familiarity with Isabelle’s proof language, all proofs are described in detail but informally. The book teaches the reader the art of precise logical reasoning and the practical use of a proof assistant as a surgical tool for formal proofs about computer science artefacts. In this sense it represents a formal approach to computer science, not just semantics. The Isabelle formalisation, including the proofs and accompanying slides, are freely available online, and the book is suitable for graduate students, advanced undergraduate students, and researchers in theoretical computer science and logic.