Mathematics for Machine Learning PDF Download
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Author: Marc Peter Deisenroth
Publisher: Cambridge University Press
ISBN: 1108569323
Category : Computers
Languages : en
Pages : 392
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Book Description
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: Marc Peter Deisenroth
Publisher: Cambridge University Press
ISBN: 1108569323
Category : Computers
Languages : en
Pages : 392
Get Book Here
Book Description
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: John C. Peterson
Publisher: Cengage Learning
ISBN: 9781133281450
Category : Mathematics
Languages : en
Pages : 0
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Book Description
Reflecting the latest technology and tools of the trade, MATHEMATICS FOR MACHINE TECHNOLOGY, 7e provides the mathematical skills and practice that students and apprentices will use on the job in the machine trades and manufacturing fields. This comprehensive book combines math concepts with relevant machine applications through industry-specific examples, realistic illustrations, and actual machine applications. Problems and examples progress from the simple to the relatively complex, from general math to trigonometry and solid geometry, and relate directly to how the math is used in machine trades and manufacturing fields. The new Seventh Edition also includes all-new units on electronic calipers, height gages, and electronic micrometers, as well as thorough coverage of measuring in both metric and customary systems. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.
Author: Robert Donald Smith
Publisher: Delmar Pub
ISBN: 9780827340435
Category : Education
Languages : en
Pages : 398
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Book Description
Author: Peterson
Publisher:
ISBN: 9781473735453
Category :
Languages : en
Pages :
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Book Description
Author: Robert D. Smith
Publisher: Cengage Learning
ISBN: 9781428336568
Category : Mathematics
Languages : en
Pages : 276
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Book Description
The new edition of this best-selling text has been reviewed and revised to clarify and update an understanding of mathematical concepts necessary for success in the machine trades and manufacturing fields. Mathematics for Machine Technology, Sixth Edition overcomes the often mechanical plug in approach found in many trade-related texts. A complete grasp of mathematical concepts are emphasized in the presentation and application of a wide-range of topics from general arithmetic processes to oblique trigonometry, compound angles, and numerical control. The material covered by this text is accompanied by realistic industry-related examples, illustrations, and actual applications, which progress from the simple to the relatively complex. Mathematics for Machine Technology, Sixth Edition provides readers with practical vocational and technical applications of mathematical concepts necessary to excel in the machine, tool-and-die, and tool design industry. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.
Author: John Vince
Publisher: Springer Science & Business Media
ISBN: 1846282837
Category : Computers
Languages : en
Pages : 251
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Book Description
This is a concise and informal introductory book on the mathematical concepts that underpin computer graphics. The author, John Vince, makes the concepts easy to understand, enabling non-experts to come to terms with computer animation work. The book complements the author's other works and is written in the same accessible and easy-to-read style. It is also a useful reference book for programmers working in the field of computer graphics, virtual reality, computer animation, as well as students on digital media courses, and even mathematics courses.
Author: R. Smith
Publisher:
ISBN: 9780827311992
Category :
Languages : en
Pages :
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Book Description
Author: Eric Lehman
Publisher:
ISBN: 9789888407064
Category : Business & Economics
Languages : en
Pages : 988
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Book Description
This book covers elementary discrete mathematics for computer science and engineering. It emphasizes mathematical definitions and proofs as well as applicable methods. Topics include formal logic notation, proof methods; induction, well-ordering; sets, relations; elementary graph theory; integer congruences; asymptotic notation and growth of functions; permutations and combinations, counting principles; discrete probability. Further selected topics may also be covered, such as recursive definition and structural induction; state machines and invariants; recurrences; generating functions.
Author: R. Smith
Publisher:
ISBN: 9780827321076
Category :
Languages : en
Pages :
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Book Description
Author: Avi Wigderson
Publisher: Princeton University Press
ISBN: 0691189137
Category : Computers
Languages : en
Pages : 434
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Book Description
An introduction to computational complexity theory, its connections and interactions with mathematics, and its central role in the natural and social sciences, technology, and philosophy Mathematics and Computation provides a broad, conceptual overview of computational complexity theory—the mathematical study of efficient computation. With important practical applications to computer science and industry, computational complexity theory has evolved into a highly interdisciplinary field, with strong links to most mathematical areas and to a growing number of scientific endeavors. Avi Wigderson takes a sweeping survey of complexity theory, emphasizing the field’s insights and challenges. He explains the ideas and motivations leading to key models, notions, and results. In particular, he looks at algorithms and complexity, computations and proofs, randomness and interaction, quantum and arithmetic computation, and cryptography and learning, all as parts of a cohesive whole with numerous cross-influences. Wigderson illustrates the immense breadth of the field, its beauty and richness, and its diverse and growing interactions with other areas of mathematics. He ends with a comprehensive look at the theory of computation, its methodology and aspirations, and the unique and fundamental ways in which it has shaped and will further shape science, technology, and society. For further reading, an extensive bibliography is provided for all topics covered. Mathematics and Computation is useful for undergraduate and graduate students in mathematics, computer science, and related fields, as well as researchers and teachers in these fields. Many parts require little background, and serve as an invitation to newcomers seeking an introduction to the theory of computation. Comprehensive coverage of computational complexity theory, and beyond High-level, intuitive exposition, which brings conceptual clarity to this central and dynamic scientific discipline Historical accounts of the evolution and motivations of central concepts and models A broad view of the theory of computation's influence on science, technology, and society Extensive bibliography
