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Maths in Focus 12 Mathematics Extension 2 is a new book written for the Mathematics Extension 2 course. Each chapter begins with a table of contents, chapter objectives and a Terminology glossary and graded exercises include HSC-style questions and realistic applications. Investigations explore the syllabus in more detail, providing ideas for research projects and modelling activities and Did you know? sections contain interesting facts and applications of the mathematics learned in a chapter. Each chapter ends with a Test Yourself revision set and Practice sets (after several chapters) include exam-style questions from various chapters. Syllabus gris and codes, answers and an index are also included to meet the new 2019 senior maths course requirements. NelsonNet resources available* Teacher Resources: ' Chapter topic tests ' Worked solutions to all questions in book ' ExamView © software and questionbank of topic questions ' Teaching program ' Chapter PDFs of the book ' Worksheets *Complimentary access to NelsonNet is available to teachers who use the accompanying student book as a core resource in their classroom. Contact your local education consultant for access codes and conditions.
Math in Focus℗ʼ is the U.S. edition of Singapore's most widely used primary program, My Pals are Here! Maths. Correlated to the Common Core Standards and aligned to the Singapore Mathematics Framework, Math in Focus℗ʼ provides world-class mathematics instruction that meets the specific needs of U.S. students.
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.