McGraw-Hill My Math, Grade K, Student Edition, Volume 2
McGraw-Hill My Math, Grade K, Student Edition, Volume 2

Author: McGraw Hill Education

Publisher: McGraw-Hill Education

Published: 2011-08-05

Total Pages: 0

ISBN-13: 9780021160679

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This set provides the consumable Student Edition, Volume 2, which contains everything students need to build conceptual understanding, application, and procedural skill and fluency with math content organized to address CCSS. Students engage in learning with write-in text on vocabulary support and homework pages, and real-world problem-solving investigations.

Caribbean Maths Connect: Book 1
Caribbean Maths Connect: Book 1

Author: Benita Byer-Bowen

Publisher: Heinemann

Published: 2006-01-17

Total Pages: 230

ISBN-13: 9780435989972

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This publication, written and developed by experienced Caribbean teachers and advisors, has been designed to meet the requirements of maths syllabuses from across the region. It includes worked examples, objectives and explanation boxes, overviews of chapters, real life case studies, assessments and reviews and progessive exercises that support the development of crtical thinking skills and independent learning among Caribbean maths students at the lower secondary level.

MyMaths: for Key Stage 3: Student Book 1B
MyMaths: for Key Stage 3: Student Book 1B

Author: David Capewell

Publisher: OUP Oxford

Published: 2013-12

Total Pages: 336

ISBN-13: 9780198304487

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MyMaths for Key Stage 3 is the brand new course that works with MyMaths to deliver the new curriculum. This student book is for middle ability students embarking on KS3. Its unique emphasis on visible progression and visual engagement, along with direct links to the MyMaths site, all help to bring maths alive for your average ability student.

A Dingo Ate My Math Book
A Dingo Ate My Math Book

Author: Burkard Polster

Publisher: American Mathematical Soc.

Published: 2017-12-27

Total Pages: 273

ISBN-13: 1470435217

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A Dingo Ate My Math Book presents ingenious, unusual, and beautiful nuggets of mathematics with a distinctly Australian flavor. It focuses, for example, on Australians' love of sports and gambling, and on Melbourne's iconic, mathematically inspired architecture. Written in a playful and humorous style, the book offers mathematical entertainment as well as a glimpse of Australian culture for the mathematically curious of all ages. This collection of engaging stories was extracted from the Maths Masters column that ran from 2007 to 2014 in Australia's Age newspaper. The maths masters in question are Burkard Polster and Marty Ross, two (immigrant) Aussie mathematicians, who each week would write about math in the news, providing a new look at old favorites, mathematical history, quirks of school mathematics—whatever took their fancy. All articles were written for a very general audience, with the intention of being as inviting as possible and assuming a minimum of mathematical background.

How I Wish I'd Taught Maths
How I Wish I'd Taught Maths

Author: Craig Barton

Publisher:

Published: 2018

Total Pages: 451

ISBN-13: 9781943920587

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Brought to an American audience for the first time, How I Wish I'd Taught Maths is the story of an experienced and successful math teacher's journey into the world of research, and how it has entirely transformed his classroom.

Mastering Essential Math Skills
Mastering Essential Math Skills

Author: Richard W. Fisher

Publisher:

Published: 2003-01-15

Total Pages: 0

ISBN-13: 9780966621112

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Provides structure and guidance to the teacher by means of speed drills, review exercises, teacher tips, word problems and new material for each day.

Mathematics for Machine Learning
Mathematics for Machine Learning

Author: Marc Peter Deisenroth

Publisher: Cambridge University Press

Published: 2020-04-23

Total Pages: 392

ISBN-13: 1108569323

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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.