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The Teacher's Book assists you with implementing Australian Signpost Maths, and includes the pedagogical background and suggested teaching program. Reproduced pages from the student book with answers are shown in the Teacher's Book to save you time correcting, with Australian Mathematics Curriculum references on each page of the Teacher's Book to help you follow how the content strands are covered. Teaching suggestions, extension work, maths language, resources and evaluations are available for each lesson, catering for differentiation and meeting individual learning needs of students. ID cards are also provided to help develop students' mathematical language and concepts, and blackline masters are given to provide extra exercises for particular areas of the syllabus. Assessment options to help discover each student's strengths and weaknesses include progress tests and diagnostic tests. Both of these assessment options supply their answers and directly reference to each concept in the student book.
AUSTRALIAN SIGNPOST MENTALS offers a complete year's work with coverage of all Australian Curriculum Maths content strands, making it ideal for consolidation of class work or use as a homework book.
The Australian Signpost Mathematics New South Wales series addresses the New South Wales Syllabus for the Australian Curriculum. This new series continues the Signpost tradition of expanding students' ability in a broad range of mathematical skills while emphasising problem-solving and working mathematically.
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.
For special education courses in schools of early childhood, primary and secondary education. Education for Inclusion and Diversity 5e continues to build on the concept of inclusive curriculum and the diversity of learning needs. This Australian text gives students a broad understanding of the principles of inclusive education, and the ways in which teachers can accommodate the differing learning needs of their students. It has been written by experts in the field of inclusion and special needs education with the particular aim of teaching students how to apply the ideas that have been presented in each chapter.