Troutman and Lichtenberg's best-selling text offers many features, chapters, and topics to reflect current research in mathematics education, the philosophy and recommendations of the NCTM, and the growing use of technology in the classroom.This highly respected text is known for its imaginative, class-tested activities, its practical teaching strategies, and its ability to explain and develop mathematical concepts clearly and succinctly. It has helped thousands of teachers and teachers-in-training gain the competence and confidence they need to develop a sound mathematics program in their K-8 classrooms that takes into account the learning needs of children.
This text for a second course in linear algebra, aimed at math majors and graduates, adopts a novel approach by banishing determinants to the end of the book and focusing on understanding the structure of linear operators on vector spaces. The author has taken unusual care to motivate concepts and to simplify proofs. For example, the book presents - without having defined determinants - a clean proof that every linear operator on a finite-dimensional complex vector space has an eigenvalue. The book starts by discussing vector spaces, linear independence, span, basics, and dimension. Students are introduced to inner-product spaces in the first half of the book and shortly thereafter to the finite- dimensional spectral theorem. A variety of interesting exercises in each chapter helps students understand and manipulate the objects of linear algebra. This second edition features new chapters on diagonal matrices, on linear functionals and adjoints, and on the spectral theorem; some sections, such as those on self-adjoint and normal operators, have been entirely rewritten; and hundreds of minor improvements have been made throughout the text.
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.
This book uses elementary versions of modern methods found in sophisticated mathematics to discuss portions of "advanced calculus" in which the subtlety of the concepts and methods makes rigor difficult to attain at an elementary level.
A brilliant tour of mathematical thought and a guide to becoming a better thinker, How Not to Be Wrong shows that math is not just a long list of rules to be learned and carried out by rote. Math touches everything we do; It's what makes the world make sense. Using the mathematician's methods and hard-won insights-minus the jargon-professor and popular columnist Jordan Ellenberg guides general readers through his ideas with rigor and lively irreverence, infusing everything from election results to baseball to the existence of God and the psychology of slime molds with a heightened sense of clarity and wonder. Armed with the tools of mathematics, we can see the hidden structures beneath the messy and chaotic surface of our daily lives. How Not to Be Wrong shows us how--Publisher's description.
"Too often, middle school and high school teachers say, ‘These students are lacking number sense.’ These books will help secondary teachers with good pedagogy to help build number sense in a creative way. Eric Milou and John SanGiovanni have created short routines that are teacher-friendly, with lots of examples, and easy to adapt to each teacher’s needs. These are the books that secondary teachers have been waiting for to help engage students in building number sense." Pamela J. Dombrowski, Secondary Math Specialist Geary County School District Junction City, KS Kickstart your high school math class! Do your students need more opportunities do develop number sense and reasoning? Are you looking to get your students energized and talking about mathematics? Have you wondered how practical, replicable, and engaging activities would complement your mathematics instruction? This guide answers the question "What could I do differently?" Taking cues from popular number sense and reasoning routines, this book gives you the rundown on how to engage in five different daily 5–10 minute routines, all of which include content-specific examples, extensions, and variations of each for algebra, functions, geometry, and data analysis. Video demonstrations allow you to see the routines in action and the book includes a year’s worth of daily instructional material that you can use to begin each class period. The routines in this book will help students Frequently revisit essential mathematical concepts Foster and shore up conceptual understanding Engage in mental mathematics, leading to efficiency and fluency Engage in mathematical discourse by constructing viable arguments and critiquing the reasoning of others Reason mathematically, and prepare for high stakes assessments Move learning beyond "correctness" by valuing mistakes and discourse and encouraging a growth mindset From trusted authors and experts Eric Milou and John SanGiovanni, this teacher-friendly resource will give you all the tools and tips you need to reinvent those critical first five or ten minutes of math class for the better!
The unifying approach of functional analysis is to view functions as points in abstract vector space and the differential and integral operators as linear transformations on these spaces. The author's goal is to present the basics of functional analysis in a way that makes them comprehensible to a student who has completed courses in linear algebra and real analysis, and to develop the topics in their historical contexts.
This text explains nontrivial applications of metric space topology to analysis. Covers metric space, point-set topology, and algebraic topology. Includes exercises, selected answers, and 51 illustrations. 1983 edition.
