How to Prove It

How to Prove It
Author: Daniel J. Velleman
Publisher: Cambridge University Press
Total Pages: 401
Release: 2006-01-16
Genre: Mathematics
ISBN: 0521861241

Many students have trouble the first time they take a mathematics course in which proofs play a significant role. This new edition of Velleman's successful text will prepare students to make the transition from solving problems to proving theorems by teaching them the techniques needed to read and write proofs. The book begins with the basic concepts of logic and set theory, to familiarize students with the language of mathematics and how it is interpreted. These concepts are used as the basis for a step-by-step breakdown of the most important techniques used in constructing proofs. The author shows how complex proofs are built up from these smaller steps, using detailed 'scratch work' sections to expose the machinery of proofs about the natural numbers, relations, functions, and infinite sets. To give students the opportunity to construct their own proofs, this new edition contains over 200 new exercises, selected solutions, and an introduction to Proof Designer software. No background beyond standard high school mathematics is assumed. This book will be useful to anyone interested in logic and proofs: computer scientists, philosophers, linguists, and of course mathematicians.

Mathematics for Machine Learning

Mathematics for Machine Learning
Author: Marc Peter Deisenroth
Publisher: Cambridge University Press
Total Pages: 392
Release: 2020-04-23
Genre: Computers
ISBN: 1108569323

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.

Probability

Probability
Author: David J. Morin
Publisher: Createspace Independent Publishing Platform
Total Pages: 0
Release: 2016
Genre: Probabilities
ISBN: 9781523318674

Preface -- Combinatorics -- Probability -- Expectation values -- Distributions -- Gaussian approximations -- Correlation and regression -- Appendices.

Introduction to Analysis

Introduction to Analysis
Author: Maxwell Rosenlicht
Publisher: Courier Corporation
Total Pages: 270
Release: 2012-05-04
Genre: Mathematics
ISBN: 0486134687

Written for junior and senior undergraduates, this remarkably clear and accessible treatment covers set theory, the real number system, metric spaces, continuous functions, Riemann integration, multiple integrals, and more. 1968 edition.

Topics in Algebra

Topics in Algebra
Author: I. N. Herstein
Publisher: John Wiley & Sons
Total Pages: 405
Release: 1991-01-16
Genre: Mathematics
ISBN: 0471010901

New edition includes extensive revisions of the material on finite groups and Galois Theory. New problems added throughout.

Student Solution Manual for Foundation Mathematics for the Physical Sciences

Student Solution Manual for Foundation Mathematics for the Physical Sciences
Author: K. F. Riley
Publisher: Cambridge University Press
Total Pages: 223
Release: 2011-03-28
Genre: Science
ISBN: 1139491970

This Student Solution Manual provides complete solutions to all the odd-numbered problems in Foundation Mathematics for the Physical Sciences. It takes students through each problem step-by-step, so they can clearly see how the solution is reached, and understand any mistakes in their own working. Students will learn by example how to arrive at the correct answer and improve their problem-solving skills.