Author: Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 268
Book Description
University of California Publications in Mathematics
Author: Publications in Mathematics
Author: University of California, BerkeleyPublisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 24
Book Description
Publications in Mathematics
Author: University of California (1868-1952)Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 473
Book Description
Berkeley Problems in Mathematics
Author: Paulo Ney de SouzaPublisher: Springer Science & Business Media
ISBN: 9780387204291
Category : Mathematics
Languages : en
Pages : 614
Book Description
This book collects approximately nine hundred problems that have appeared on the preliminary exams in Berkeley over the last twenty years. It is an invaluable source of problems and solutions. Readers who work through this book will develop problem solving skills in such areas as real analysis, multivariable calculus, differential equations, metric spaces, complex analysis, algebra, and linear algebra.
Research in Collegiate Mathematics Education III
Author: James J. KaputPublisher: American Mathematical Soc.
ISBN: 0821808826
Category : Education
Languages : en
Pages : 330
Book Description
Volume 3 of Research in Collegiate Mathematics Education (RCME) presents state-of-the-art research on understanding, teaching and learning mathematics at the post-secondary level. This volume contains information on methodology and research concentrating on these areas of student learning: Problem Solving; Understanding Concepts; and Understanding Proofs.
An Introduction to Mathematical Reasoning
Author: Peter J. EcclesPublisher: Cambridge University Press
ISBN: 1139632566
Category : Mathematics
Languages : en
Pages : 364
Book Description
This book eases students into the rigors of university mathematics. The emphasis is on understanding and constructing proofs and writing clear mathematics. The author achieves this by exploring set theory, combinatorics, and number theory, topics that include many fundamental ideas and may not be a part of a young mathematician's toolkit. This material illustrates how familiar ideas can be formulated rigorously, provides examples demonstrating a wide range of basic methods of proof, and includes some of the all-time-great classic proofs. The book presents mathematics as a continually developing subject. Material meeting the needs of readers from a wide range of backgrounds is included. The over 250 problems include questions to interest and challenge the most able student but also plenty of routine exercises to help familiarize the reader with the basic ideas.
Mathematics at Berkeley
Author: Calvin C. MoorePublisher: A K Peters/CRC Press
ISBN:
Category : Education
Languages : en
Pages : 382
Book Description
In this fascinating history of the mathematics department at the University of California, Berkeley, Moore describes how this institution evolved from a single facutly member at a financially-troubled private college into a major research center that is ranked among the very best in the USA and in the world. Moore's account spans from its origins in the 1850s to the establishment and early years of the Mathematical Sciences Research Institute (MSRI) in the early to mid 1980s.
University of California Publications in Mathematics
Author: University of California Publications
Author: University of California, BerkeleyPublisher:
ISBN:
Category :
Languages : en
Pages : 96
Book Description
Mathematics for Machine Learning
Author: Marc Peter DeisenrothPublisher: 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.