Challenging Units for Gifted Learners
Challenging Units for Gifted Learners

Author: Kenneth J. Smith

Publisher: Taylor & Francis

Published: 2021-09-03

Total Pages: 211

ISBN-13: 1000498107

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Gifted students have the potential to learn material earlier and faster, to handle more complexity and abstraction, and to solve complex problems better. This potential, however, needs stimulating experiences from home and school or it will not unfold. These books are designed to help teachers provide the stimulating curricula that will nurture this potential in school. The units presented in this series are based on research into how these students actually think differently from their peers and how they use their learning styles and potential not merely to develop intellectual expertise, but to move beyond expertise to the production of new ideas. The Math book includes units that ask students to develop a financial portfolio that includes high- and low-risk stocks, options and margins, AAA and junk bonds, mutual funds, and money markets; use math, science, engineering, technology, and art to design and build a miniature golf course; develop games based on probability; and run a real-life small business. Grades 6-8

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.