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Author: Joel Robbin Publisher: ISBN: Category : Languages : en Pages : 133
Book Description
This is an extensive textbook and study manual for University Level First Semester Calculus (Math 221). The Book has concepts, problems and solutions for the topic.
Author: Theodore Shifrin Publisher: Macmillan ISBN: 1429215216 Category : Mathematics Languages : en Pages : 394
Book Description
Linear Algebra: A Geometric Approach, Second Edition, presents the standard computational aspects of linear algebra and includes a variety of intriguing interesting applications that would be interesting to motivate science and engineering students, as well as help mathematics students make the transition to more abstract advanced courses. The text guides students on how to think about mathematical concepts and write rigorous mathematical arguments.
Author: Joel Robbin Publisher: ISBN: Category : Languages : en Pages : 133
Book Description
This is an extensive textbook and study manual for University Level First Semester Calculus (Math 221). The Book has concepts, problems and solutions for the topic.
Author: László Lovász Publisher: Springer Science & Business Media ISBN: 0387217770 Category : Mathematics Languages : en Pages : 344
Book Description
Aimed at undergraduate mathematics and computer science students, this book is an excellent introduction to a lot of problems of discrete mathematics. It discusses a number of selected results and methods, mostly from areas of combinatorics and graph theory, and it uses proofs and problem solving to help students understand the solutions to problems. Numerous examples, figures, and exercises are spread throughout the book.
Author: James Stewart Publisher: Thomson Brooks/Cole ISBN: 9780495012399 Category : Mathematics Languages : en Pages : 0
Book Description
This helpful guide contains a short list of key concepts; a short list of skills to master; a brief introduction to the ideas of the section; an elaboration of the concepts and skills, including extra worked-out examples; and links in the margin to earlier and later material in the text and Study Guide.
Author: Marc Peter Deisenroth Publisher: Cambridge University Press ISBN: 1108569323 Category : Computers Languages : en Pages : 392
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: Joel Robbin
Author: Theodore Shifrin
Author: Joel Robbin
Author: Sigurd Angenent
Author: László Lovász
Author: Georgia B. Pyrros
Author: James Stewart
Author: Marc Peter Deisenroth
Author: Georgia Pyrros
Author: United States Air Force Academy
Author: University of Michigan