Contexts For Learning Mathematics Level 1 Read Alouds
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Contexts for Learning Mathematics Level 1 Read-Alouds
Author: Catherine Twomey Fosnot
language: en
Publisher: Firsthand Books
Release Date: 2007-04
Building learning around rich, instructionally sound contexts was an overarching goal during the development of the Contexts for Learning Mathematics series. Throughout the series context is used to set the stage for learning. It establishes a terrain that will intrigue children and ignite their imaginations. The contexts are situations children can imagine - either realistic or fictional - that enable them to reflect on what they are doing and apply mathematical thinking to their own world. Contexts for investigations are typically developed with stories and pictures. These are carefully crafted to involve students in meaningful investigations of the big ideas, strategies, and models that shape mathematical thinking. - The images and texts are engaging and include age-appropriate children using mathematics to solve real-world problems. - The numbers referenced represent landmark numbers or number relationships that are significant and telling. - The models and metaphors within a context make relationships and strategies more tangible and explicit. The contexts for the eight units in Investigating Number Sense, Addition, and Subtraction (Grades K - 3) are established through eight engaging read-aloud books (15" x 12") that meld humor, intrigue, and good math sense. To learn more visit www.contextsforlearning.com
Mathematics for Machine Learning
Author: Marc Peter Deisenroth
language: en
Publisher: Cambridge University Press
Release Date: 2020-04-23
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.
Literature-Based Teaching in the Content Areas
Forty classroom-tested, classroom-ready literature-based strategies for teaching in the K–8 content areas Grounded in theory and best-practices research, this practical text provides teachers with 40 strategies for using fiction and non-fiction trade books to teach in five key content areas: language arts and reading, social studies, mathematics, science, and the arts. Each strategy provides everything a teacher needs to get started: a classroom example that models the strategy, a research-based rationale, relevant content standards, suggested books, reader-response questions and prompts, assessment ideas, examples of how to adapt the strategy for different grade levels (K–2, 3–5, and 6–8), and ideas for differentiating instruction for English language learners and struggling students. Throughout the book, student work samples and classroom vignettes bring the content to life.