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Download or read book Mathematics for Machine Learning written by Marc Peter Deisenroth and published by Cambridge University Press. This book was released on 2020-04-23 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

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Download or read book Using Technology with Classroom Instruction That Works written by Howard Pitler and published by ASCD. This book was released on 2012-08-02 with total page 265 pages. Available in PDF, EPUB and Kindle. Book excerpt: Technology is ubiquitous, and its potential to transform learning is immense. The first edition of Using Technology with Classroom Instruction That Works answered some vital questions about 21st century teaching and learning: What are the best ways to incorporate technology into the curriculum? What kinds of technology will best support particular learning tasks and objectives? How does a teacher ensure that technology use will enhance instruction rather than distract from it? This revised and updated second edition of that best-selling book provides fresh answers to these critical questions, taking into account the enormous technological advances that have occurred since the first edition was published, including the proliferation of social networks, mobile devices, and web-based multimedia tools. It also builds on the up-to-date research and instructional planning framework featured in the new edition of Classroom Instruction That Works, outlining the most appropriate technology applications and resources for all nine categories of effective instructional strategies: * Setting objectives and providing feedback * Reinforcing effort and providing recognition * Cooperative learning * Cues, questions, and advance organizers * Nonlinguistic representations * Summarizing and note taking * Assigning homework and providing practice * Identifying similarities and differences * Generating and testing hypotheses Each strategy-focused chapter features examples—across grade levels and subject areas, and drawn from real-life lesson plans and projects—of teachers integrating relevant technology in the classroom in ways that are engaging and inspiring to students. The authors also recommend dozens of word processing applications, spreadsheet generators, educational games, data collection tools, and online resources that can help make lessons more fun, more challenging, and—most of all—more effective.

Download or read book All of Statistics written by Larry Wasserman and published by Springer Science & Business Media. This book was released on 2013-12-11 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.

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Download or read book Probability written by Rick Durrett and published by Cambridge University Press. This book was released on 2010-08-30 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This classic introduction to probability theory for beginning graduate students covers laws of large numbers, central limit theorems, random walks, martingales, Markov chains, ergodic theorems, and Brownian motion. It is a comprehensive treatment concentrating on the results that are the most useful for applications. Its philosophy is that the best way to learn probability is to see it in action, so there are 200 examples and 450 problems. The fourth edition begins with a short chapter on measure theory to orient readers new to the subject.