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: Margaret Lial
Publisher: Pearson Higher Ed
ISBN: 1292075813
Category :
Languages : en
Pages : 674
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Book Description
Steadfast Support for Your Evolving Course. Essentials of College Algebra, Eleventh Edition, by Lial, Hornsby, Schneider, and Daniels, develops both the conceptual understanding and the analytical skills necessary for success in mathematics. With the Eleventh Edition, the authors have adapted and updated the program for the evolving student, New co-author Callie Daniels brings her experience with traditional, hybrid, and online courses, to create a suite of resources to support today’s learners. This program provides a better teaching and learning experience–for you and your students. Here’s how: Support for learning concepts: a systematic approach is used to present each topic, and is designed to actively engage students in the learning process. The variety of exercise types promotes understanding of the concepts and reduces the opportunity for rote memorization. Support for review and test preparation: ample opportunities for review are interspersed throughout and at the end of chapters. MyMathLab® is not included. Students, if MyMathLab is a recommended/mandatory component of the course, please ask your instructor for the correct ISBN. MyMathLab should only be purchased when required by an instructor. Instructors, contact your Pearson representative for more information. MyMathLab is an online homework, tutorial, and assessment product designed to personalize learning and improve results. With a wide range of interactive, engaging, and assignable activities, students are encouraged to actively learn and retain tough course concepts.
Author: Allen R. Angel
Publisher: Addison-Wesley Longman
ISBN: 9780321205650
Category : Mathematics
Languages : en
Pages : 0
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Book Description
This best-selling text balances solid mathematical coverage with a comprehensive overview of mathematical concepts as they relate to varied disciplines. This text provides an appreciation of mathematics, highlighting mathematical history, and applications of math to the arts and sciences. It is an ideal book for students who require a general overview of mathematics, especially those majoring in liberal arts, the social sciences, business, nursing and allied health fields. A Survey of Mathematics with Applications is now available in an expanded edition with extra chapters on graph theory (Chapter 14) and voting and apportionment (Chapter 15).
Author: Thomas A. Romberg
Publisher: SUNY Press
ISBN: 9780791408995
Category : Mathematics
Languages : en
Pages : 380
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Book Description
Are current testing practices consistent with the goals of the reform movement in school mathematics? If not, what are the alternatives? How can authentic performance in mathematics be assessed? These and similar questions about tests and their uses have forced those advocating change to examine the way in which mathematical performance data is gathered and used in American schools. This book provides recent views on the issues surrounding mathematics tests, such as the need for valid performance data, the implications of the Curriculum and Evaluation Standards for School Mathematics for test development, the identification of valid items and tests in terms of the Standards, the procedures now being used to construct a sample of state assessment tests, gender differences in test taking, and methods of reporting student achievement.
Author: Laurence D. Hoffmann
Publisher:
ISBN: 9780071108218
Category : Calculus
Languages : en
Pages :
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Book Description
Calculus for Business, Economics, and the Social and Life Sciences introduces calculus in real-world contexts and provides a sound, intuitive understanding of the basic concepts students need as they pursue careers in business, the life sciences, and the social sciences. The new Ninth Edition builds on the straightforward writing style, practical applications from a variety of disciplines, clear step-by-step problem solving techniques, and comprehensive exercise sets that have been hallmarks of Hoffmann/Bradley's success through the years.
Author: G. Jay Kerns
Publisher: Lulu.com
ISBN: 0557249791
Category : Education
Languages : en
Pages : 388
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Book Description
This is a textbook for an undergraduate course in probability and statistics. The approximate prerequisites are two or three semesters of calculus and some linear algebra. Students attending the class include mathematics, engineering, and computer science majors.
Author: Stefan Buttcher
Publisher: MIT Press
ISBN: 0262528878
Category : Computers
Languages : en
Pages : 633
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Book Description
An introduction to information retrieval, the foundation for modern search engines, that emphasizes implementation and experimentation. Information retrieval is the foundation for modern search engines. This textbook offers an introduction to the core topics underlying modern search technologies, including algorithms, data structures, indexing, retrieval, and evaluation. The emphasis is on implementation and experimentation; each chapter includes exercises and suggestions for student projects. Wumpus—a multiuser open-source information retrieval system developed by one of the authors and available online—provides model implementations and a basis for student work. The modular structure of the book allows instructors to use it in a variety of graduate-level courses, including courses taught from a database systems perspective, traditional information retrieval courses with a focus on IR theory, and courses covering the basics of Web retrieval. In addition to its classroom use, Information Retrieval will be a valuable reference for professionals in computer science, computer engineering, and software engineering.
Author: William L. Briggs
Publisher: Addison-Wesley Longman
ISBN: 9780321664150
Category : Calculus
Languages : en
Pages : 0
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Book Description
Drawing on their decades of teaching experience, William Briggs and Lyle Cochran have created a calculus text that carries the teacher's voice beyond the classroom. That voice-evident in the narrative, the figures, and the questions interspersed in the narrative-is a master teacher leading readers to deeper levels of understanding. The authors appeal to readers' geometric intuition to introduce fundamental concepts and lay the foundation for the more rigorous development that follows. Comprehensive exercise sets have received praise for their creativity, quality, and scope. Note: This is the standalone book if you want the book/access card order the ISBN below: 0321665880 / 9780321665881 Multivariable Calculus Plus MyMathLab -- Access Card Package Package consists of: 0321431308 / 9780321431301 MyMathLab/MyStatLab -- Glue-in Access Card 0321654064 / 9780321654069 MyMathLab Inside Star Sticker 0321664159 / 9780321664150 Multivariable Calculus
Author: Margaret L. Lial
Publisher: Addison Wesley
ISBN: 9780673983367
Category : Mathematics
Languages : en
Pages : 500
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Book Description
Author: Gary W. Oehlert
Publisher: W. H. Freeman
ISBN: 9780716735106
Category : Mathematics
Languages : en
Pages : 600
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Book Description
Oehlert's text is suitable for either a service course for non-statistics graduate students or for statistics majors. Unlike most texts for the one-term grad/upper level course on experimental design, Oehlert's new book offers a superb balance of both analysis and design, presenting three practical themes to students: • when to use various designs • how to analyze the results • how to recognize various design options Also, unlike other older texts, the book is fully oriented toward the use of statistical software in analyzing experiments.
