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With its conversational tone and practical focus, this text mixes applied and theoretical aspects for a solid introduction to cryptography and security, including the latest significant advancements in the field. Assumes a minimal background. The level of math sophistication is equivalent to a course in linear algebra. Presents applications and protocols where cryptographic primitives are used in practice, such as SET and SSL. Provides a detailed explanation of AES, which has replaced Feistel-based ciphers (DES) as the standard block cipher algorithm. Includes expanded discussions of block ciphers, hash functions, and multicollisions, plus additional attacks on RSA to make readers aware of the strengths and shortcomings of this popular scheme. For engineers interested in learning more about cryptography.

This book assumes a minimal background in programming and a level of math sophistication equivalent to a course in linear algebra. It provides a flexible organization, as each chapter is modular and can be covered in any order. Using Mathematica, Maple, and MATLAB, computer examples included in an Appendix explain how to do computation and demonstrate important concepts. A full chapter on error correcting codes introduces the basic elements of coding theory. Other topics covered: Classical cryptosystems, basic number theory, the data encryption standard, AES: Rijndael, the RSA algorithm, discrete logarithms, digital signatures, e-commerce and digital cash, secret sharing schemes, games, zero knowledge techniques, key establishment protocols, information theory, elliptic curves, error correcting codes, quantum cryptography. For professionals in cryptography and network security.

Coding and computational thinking are among the skills that will serve students well in the future. Coding goes beyond websites and software; it is an essential component in finding solutions to everyday problems. Computational thinking has many applications beyond the computer lab or math class. It teaches reasoning, creativity and expression, and is an innovative way to demonstrate content knowledge and see mathematical processes in action.No-Fear Coding shows K-5 educators how to bring coding into their curriculum by embedding computational thinking skills into activities for every content area. At the same time, embedding these skills helps students prepare for coding in the middle grades as they build their knowledge.To help teachers easily and effectively introduce coding, the book features: classroom-tested lessons and activities designed for skills progression; ready-to-implement coding exercises that can be incorporated across the curriculum; alignment to ISTE and Computer Science Teachers Association (CSTA) standards; and case studies and explorations of technology tools and resources to teach coding.

With its conversational tone and practical focus, this text mixes applied and theoretical aspects for a solid introduction to cryptography and security, including the latest significant advancements in the field. Assumes a minimal background. The level of math sophistication is equivalent to a course in linear algebra. Presents applications and protocols where cryptographic primitives are used in practice, such as SET and SSL. Provides a detailed explanation of AES, which has replaced Feistel-based ciphers (DES) as the standard block cipher algorithm. Includes expanded discussions of block ciphers, hash functions, and multicollisions, plus additional attacks on RSA to make readers aware of the strengths and shortcomings of this popular scheme. For engineers interested in learning more about cryptography.

Probability and Statistics for Data Science: Math + R + Data covers "math stat"―distributions, expected value, estimation etc.―but takes the phrase "Data Science" in the title quite seriously: * Real datasets are used extensively. * All data analysis is supported by R coding. * Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks. * Leads the student to think critically about the "how" and "why" of statistics, and to "see the big picture." * Not "theorem/proof"-oriented, but concepts and models are stated in a mathematically precise manner. Prerequisites are calculus, some matrix algebra, and some experience in programming. Norman Matloff is a professor of computer science at the University of California, Davis, and was formerly a statistics professor there. He is on the editorial boards of the Journal of Statistical Software and The R Journal. His book Statistical Regression and Classification: From Linear Models to Machine Learning was the recipient of the Ziegel Award for the best book reviewed in Technometrics in 2017. He is a recipient of his university's Distinguished Teaching Award.

Probability and Statistics for Data Science: Math + R + Data covers "math stat"―distributions, expected value, estimation etc.―but takes the phrase "Data Science" in the title quite seriously: * Real datasets are used extensively. * All data analysis is supported by R coding. * Includes many Data Science applications, such as PCA, mixture distributions, random graph models, Hidden Markov models, linear and logistic regression, and neural networks. * Leads the student to think critically about the "how" and "why" of statistics, and to "see the big picture." * Not "theorem/proof"-oriented, but concepts and models are stated in a mathematically precise manner. Prerequisites are calculus, some matrix algebra, and some experience in programming. Norman Matloff is a professor of computer science at the University of California, Davis, and was formerly a statistics professor there. He is on the editorial boards of the Journal of Statistical Software and The R Journal. His book Statistical Regression and Classification: From Linear Models to Machine Learning was the recipient of the Ziegel Award for the best book reviewed in Technometrics in 2017. He is a recipient of his university's Distinguished Teaching Award.

Get to know the ‘why’ and ‘how’ of machine learning and big data in quantitative investment Big Data and Machine Learning in Quantitative Investment is not just about demonstrating the maths or the coding. Instead, it’s a book by practitioners for practitioners, covering the questions of why and how of applying machine learning and big data to quantitative finance. The book is split into 13 chapters, each of which is written by a different author on a specific case. The chapters are ordered according to the level of complexity; beginning with the big picture and taxonomy, moving onto practical applications of machine learning and finally finishing with innovative approaches using deep learning. ·    Gain a solid reason to use machine learning ·    Frame your question using financial markets laws ·    Know your data·    Understand how machine learning is becoming ever more sophisticated Machine learning and big data are not a magical solution, but appropriately applied, they are extremely effective tools for quantitative investment — and this book shows you how.

Right-Brained Multiplication & Division strongly utilizes right brained elements to help children learn and remember their math facts. Additional elements in this edition include full color images, color-coding within the instructional pages, clear directions for the teacher or parent, hands-on practice, skits to act out, and practice problems complete with answer key. This approach will show students what multiplication & division means, will relate the two skills to each other, will present the facts via stories and images to help provide hooks for learning and recall. This book is quickly becoming our best-selling math book because so many children struggle to memorize multiplication and division facts! Right-brained elements included in this book: · embedded symbols in VISUALS · use of PERSONIFICATION · PATTERN DISCOVERY · use of STORY · COLOR CODING · BODY MOTION Paperback: 124 pages. Full color.

Your guide to the functional programming paradigm  Functional programming mainly sees use in math computations, including those used in Artificial Intelligence and gaming. This programming paradigm makes algorithms used for math calculations easier to understand and provides a concise method of coding algorithms by people who aren't developers. Current books on the market have a significant learning curve because they're written for developers, by developers—until now.  Functional Programming for Dummies explores the differences between the pure (as represented by the Haskell language) and impure (as represented by the Python language) approaches to functional programming for readers just like you. The pure approach is best suited to researchers who have no desire to create production code but do need to test algorithms fully and demonstrate their usefulness to peers. The impure approach is best suited to production environments because it's possible to mix coding paradigms in a single application to produce a result more quickly. Functional Programming For Dummies uses this two-pronged approach to give you an all-in-one approach to a coding methodology that can otherwise be hard to grasp. Learn pure and impure when it comes to coding Dive into the processes that most functional programmers use to derive, analyze and prove the worth of algorithms Benefit from examples that are provided in both Python and Haskell Glean the expertise of an expert author who has written some of the market-leading programming books to date If you’re ready to massage data to understand how things work in new ways, you’ve come to the right place!

Right-Brained Multiplication & Division strongly utilizes right brained elements to help children learn and remember their math facts. Additional elements in this edition include full color images, color-coding within the instructional pages, clear directions for the teacher or parent, hands-on practice, skits to act out, and practice problems complete with answer key. This approach will show students what multiplication & division means, will relate the two skills to each other, will present the facts via stories and images to help provide hooks for learning and recall. This book is quickly becoming our best-selling math book because so many children struggle to memorize multiplication and division facts! Right-brained elements included in this book: · embedded symbols in VISUALS · use of PERSONIFICATION · PATTERN DISCOVERY · use of STORY · COLOR CODING · BODY MOTION Paperback: 124 pages. Full color.

Get to know the ‘why’ and ‘how’ of machine learning and big data in quantitative investment Big Data and Machine Learning in Quantitative Investment is not just about demonstrating the maths or the coding. Instead, it’s a book by practitioners for practitioners, covering the questions of why and how of applying machine learning and big data to quantitative finance. The book is split into 13 chapters, each of which is written by a different author on a specific case. The chapters are ordered according to the level of complexity; beginning with the big picture and taxonomy, moving onto practical applications of machine learning and finally finishing with innovative approaches using deep learning. ·    Gain a solid reason to use machine learning ·    Frame your question using financial markets laws ·    Know your data·    Understand how machine learning is becoming ever more sophisticated Machine learning and big data are not a magical solution, but appropriately applied, they are extremely effective tools for quantitative investment — and this book shows you how.