9781108455145 - Mathematics for Machine Learning von Deisenroth, Marc Peter (11 Ergebnisse)

Zustand: Very Good. Item in very good condition! Textbooks may not include supplemental items i.e. CDs, access codes etc.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New.

Zustand: New. In.

Zustand: New. 2020. Paperback. . . . . . Books ship from the US and Ireland.

Paperback. Zustand: Brand New. 398 pages. 9.25x6.25x0.25 inches. In Stock.

Zustand: New. This self-contained textbook introduces all the relevant mathematical concepts needed to understand and use machine learning methods, with a minimum of prerequisites. Topics include linear algebra, analytic geometry, matrix decompositions, vector calculus, .

Taschenbuch. Zustand: Neu. Neuware - 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.

Taschenbuch. Zustand: Neu. Neuware -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 studentsand otherswith a mathematical background, these derivations provide a starting point to machine learning texts. Forthoselearning 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.Libri GmbH, Europaallee 1, 36244 Bad Hersfeld 371 pp. Englisch.

Taschenbuch. Zustand: Neu. Mathematics for Machine Learning | Marc Peter Deisenroth (u. a.) | Taschenbuch | Kartoniert / Broschiert | Englisch | 2020 | Cambridge University Pr. | EAN 9781108455145 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.

Taschenbuch. Zustand: Neu. Neuware -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 studentsand otherswith a mathematical background, these derivations provide a starting point to machine learning texts. Forthoselearning 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. 371 pp. Englisch.