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.

Transforming the Workforce for Children Birth Through Age 8
Transforming the Workforce for Children Birth Through Age 8

Author: National Research Council

Publisher: National Academies Press

Published: 2015-07-23

Total Pages: 587

ISBN-13: 0309324882

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Children are already learning at birth, and they develop and learn at a rapid pace in their early years. This provides a critical foundation for lifelong progress, and the adults who provide for the care and the education of young children bear a great responsibility for their health, development, and learning. Despite the fact that they share the same objective - to nurture young children and secure their future success - the various practitioners who contribute to the care and the education of children from birth through age 8 are not acknowledged as a workforce unified by the common knowledge and competencies needed to do their jobs well. Transforming the Workforce for Children Birth Through Age 8 explores the science of child development, particularly looking at implications for the professionals who work with children. This report examines the current capacities and practices of the workforce, the settings in which they work, the policies and infrastructure that set qualifications and provide professional learning, and the government agencies and other funders who support and oversee these systems. This book then makes recommendations to improve the quality of professional practice and the practice environment for care and education professionals. These detailed recommendations create a blueprint for action that builds on a unifying foundation of child development and early learning, shared knowledge and competencies for care and education professionals, and principles for effective professional learning. Young children thrive and learn best when they have secure, positive relationships with adults who are knowledgeable about how to support their development and learning and are responsive to their individual progress. Transforming the Workforce for Children Birth Through Age 8 offers guidance on system changes to improve the quality of professional practice, specific actions to improve professional learning systems and workforce development, and research to continue to build the knowledge base in ways that will directly advance and inform future actions. The recommendations of this book provide an opportunity to improve the quality of the care and the education that children receive, and ultimately improve outcomes for children.

Teacher Quality, Instructional Quality and Student Outcomes
Teacher Quality, Instructional Quality and Student Outcomes

Author: Trude Nilsen

Publisher: Springer

Published: 2016-09-19

Total Pages: 167

ISBN-13: 3319412523

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This volume offers insights from modeling relations between teacher quality, instructional quality and student outcomes in mathematics across countries. The relations explored take the educational context, such as school climate, into account. The International Association for the Evaluation of Educational Achievement’s Trends in Mathematics and Science Study (TIMSS) is the only international large-scale study possessing a design framework that enables investigation of relations between teachers, their teaching, and student outcomes in mathematics. TIMSS provides both student achievement data and contextual background data from schools, teachers, students and parents, for over 60 countries. This book makes a major contribution to the field of educational effectiveness, especially teaching effectiveness, where cross-cultural comparisons are scarce. For readers interested in teacher quality, instructional quality, and student achievement and motivation in mathematics, the comparisons across cultures, grades, and time are insightful and thought-provoking. For readers interested in methodology, the advanced analytical methods, combined with application of methods new to educational research, illustrate interesting novel directions in methodology and the secondary analysis of international large-scale assessment (ILSA).