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Pure Mathematics for Beginners - Solution GuideThis book contains complete solutions to the problems in the 16 Problem Sets in Pure Mathematics for Beginners. Note that this book references examples and theorems from Pure Mathematics for Beginners. Therefore, it is strongly suggested that you purchase a copy of that book before purchasing this one.
This lively introductory text exposes the student in the humanities to the world of discrete mathematics. A problem-solving based approach grounded in the ideas of George Pólya are at the heart of this book. Students learn to handle and solve new problems on their own. A straightforward, clear writing style and well-crafted examples with diagrams invite the students to develop into precise and critical thinkers. Particular attention has been given to the material that some students find challenging, such as proofs. This book illustrates how to spot invalid arguments, to enumerate possibilities, and to construct probabilities. It also presents case studies to students about the possible detrimental effects of ignoring these basic principles. The book is invaluable for a discrete and finite mathematics course at the freshman undergraduate level or for self-study since there are full solutions to the exercises in an appendix.'Written with clarity, humor and relevant real-world examples, Basic Discrete Mathematics is a wonderful introduction to discrete mathematical reasoning.'- Arthur Benjamin, Professor of Mathematics at Harvey Mudd College, and author of The Magic of Math
This textbook covers in one volume all topics required in the pure mathematics section of single subject A-Level Mathematics syllabuses in the UK, as well as a significant part of the work required by those studying for Further Mathematics and for A-Level
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.
A groundbreaking introduction to vectors, matrices, and least squares for engineering applications, offering a wealth of practical examples.