. This series, well-known for accessibility and for a student-friendly approach, has a wealth of features: Worked Examples, Activities, Investigations, Graded Exercises, Key Points summaries and Discussion Points. To ensure exam success there are plenty of up-to-date exam questions, plus warning signs to indicate common pitfalls. MEI offer full support to schools through their network with newsletters, training days and an annual conference. Numerical Methods is an AS Further Maths module.
This title should help students following the MEI syllabus to enhance their portfolio of evidence for the key skills, as well as enhancing their university profiles. It contains activities, investigations and graded exercises to ensure comprehensive and varied study.
This series, well-known for accessibility and for a student-friendly approach, has a wealth of features: worked examples, activities, investigations, graded exercises, Key Points summaries and Discussion Points. To ensure exam success there are plenty of up-to-date exam questions, plus warning signs to indicate common pitfalls. MEI offer full support to schools through their network with newsletters, training days and an annual conference.
From a review of the second edition: "This book covers many interesting topics not usually covered in a present day undergraduate course, as well as certain basic topics such as the development of the calculus and the solution of polynomial equations. The fact that the topics are introduced in their historical contexts will enable students to better appreciate and understand the mathematical ideas involved...If one constructs a list of topics central to a history course, then they would closely resemble those chosen here." (David Parrott, Australian Mathematical Society) This book offers a collection of historical essays detailing a large variety of mathematical disciplines and issues; it’s accessible to a broad audience. This third edition includes new chapters on simple groups and new sections on alternating groups and the Poincare conjecture. Many more exercises have been added as well as commentary that helps place the exercises in context.
Exam Board: MEI Level: A-level Subject: Mathematics First Teaching: September 2017 First Exam: June 2018 An OCR endorsed textbook Encourage every student to develop a deeper understanding of mathematical concepts and their applications with textbooks that draw on the well-known MEI (Mathematics in Education and Industry) series, updated and tailored to the 2017 OCR (MEI) specification and developed by subject experts and MEI. - Develop problem-solving, proof and modelling skills with plenty of questions and well-structured exercises that build skills and mathematical techniques. - Build connections between topics, using real-world contexts to help develop mathematical modelling skills, thus providing a fuller and more coherent understanding of mathematical concepts. - Prepare students for assessment with practice questions written by subject experts. - Ensure coverage of the new statistics requirements with five dedicated statistics chapters and questions around the use of large data sets. - Supports the use of technology with a variety of questions based around the use of spreadsheets, graphing software and graphing calculators. - Provide clear paths of progression that combine pure and applied maths into a coherent whole.
Develop a deeper understanding of mathematical concepts and their applications with new and updated editions from our bestselling series. - Build connections between topics using real-world contexts that develop mathematical modelling skills, thus providing your students with a fuller and more coherent understanding of mathematical concepts. - Develop fluency in problem-solving, proof and modelling with plenty of questions and well-structured exercises. - Overcome misconceptions and develop mathematical insight with annotated worked examples. - Enhance understanding and map your progress with graduated exercises that support you at every stage of your learning.
Waves in Oceanic and Coastal Waters describes the observation, analysis and prediction of wind-generated waves in the open ocean, in shelf seas, and in coastal regions with islands, channels, tidal flats and inlets, estuaries, fjords and lagoons. Most of this richly illustrated book is devoted to the physical aspects of waves. After introducing observation techniques for waves, both at sea and from space, the book defines the parameters that characterise waves. Using basic statistical and physical concepts, the author discusses the prediction of waves in oceanic and coastal waters, first in terms of generalised observations, and then in terms of the more theoretical framework of the spectral energy balance. He gives the results of established theories and also the direction in which research is developing. The book ends with a description of SWAN (Simulating Waves Nearshore), the preferred computer model of the engineering community for predicting waves in coastal waters.
Mallat's book is the undisputed reference in this field - it is the only one that covers the essential material in such breadth and depth. - Laurent Demanet, Stanford University The new edition of this classic book gives all the major concepts, techniques and applications of sparse representation, reflecting the key role the subject plays in today's signal processing. The book clearly presents the standard representations with Fourier, wavelet and time-frequency transforms, and the construction of orthogonal bases with fast algorithms. The central concept of sparsity is explained and applied to signal compression, noise reduction, and inverse problems, while coverage is given to sparse representations in redundant dictionaries, super-resolution and compressive sensing applications. Features: * Balances presentation of the mathematics with applications to signal processing * Algorithms and numerical examples are implemented in WaveLab, a MATLAB toolbox New in this edition * Sparse signal representations in dictionaries * Compressive sensing, super-resolution and source separation * Geometric image processing with curvelets and bandlets * Wavelets for computer graphics with lifting on surfaces * Time-frequency audio processing and denoising * Image compression with JPEG-2000 * New and updated exercises A Wavelet Tour of Signal Processing: The Sparse Way, Third Edition, is an invaluable resource for researchers and R&D engineers wishing to apply the theory in fields such as image processing, video processing and compression, bio-sensing, medical imaging, machine vision and communications engineering. Stephane Mallat is Professor in Applied Mathematics at École Polytechnique, Paris, France. From 1986 to 1996 he was a Professor at the Courant Institute of Mathematical Sciences at New York University, and between 2001 and 2007, he co-founded and became CEO of an image processing semiconductor company. Includes all the latest developments since the book was published in 1999, including its application to JPEG 2000 and MPEG-4 Algorithms and numerical examples are implemented in Wavelab, a MATLAB toolbox Balances presentation of the mathematics with applications to signal processing
Exam Board: MEI Level: A-level Subject: Mathematics First Teaching: September 2017 First Exam: June 2018 An OCR endorsed textbook Help students to develop their knowledge and apply their reasoning to mathematical problems with textbooks that draw on the well-known MEI (Mathematics in Education and Industry) series, updated and tailored to the 2017 OCR (MEI) specification and developed by subject experts and MEI. - Ensure targeted development of reasoning and problem-solving skills with plenty of practice questions and structured exercises that build mathematical skills and techniques. - Build connections between topics, using real-world contexts to help develop mathematical modelling skills, thus providing a fuller and more coherent understanding of mathematical concepts. - Address the new statistics requirements with five dedicated statistics chapters and questions around the use of large data sets. - Help students to overcome misconceptions and develop insight into problem solving with annotated worked examples. - Develop understanding and measure progress with graduated exercises that support students at every stage of their learning. - Provide clear paths of progression that combine pure and applied maths into a coherent whole.
Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of “recipes,” this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques. Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.
Discrete optimization problems are everywhere, from traditional operations research planning (scheduling, facility location and network design); to computer science databases; to advertising issues in viral marketing. Yet most such problems are NP-hard; unless P = NP, there are no efficient algorithms to find optimal solutions. This book shows how to design approximation algorithms: efficient algorithms that find provably near-optimal solutions. The book is organized around central algorithmic techniques for designing approximation algorithms, including greedy and local search algorithms, dynamic programming, linear and semidefinite programming, and randomization. Each chapter in the first section is devoted to a single algorithmic technique applied to several different problems, with more sophisticated treatment in the second section. The book also covers methods for proving that optimization problems are hard to approximate. Designed as a textbook for graduate-level algorithm courses, it will also serve as a reference for researchers interested in the heuristic solution of discrete optimization problems.
Naturally occurring or manufactured through chemical and/or physical processes, particulate materials are substances consisting of individual particles which have significance to the global economy, society and environments. Due to the diversity and intrinsic nature, manufacturing, handling and processing of particulate materials still face numerous challenges. Aimed at addressing these challenges, this book contains a selection of papers discussing the state-of-the-art research in particulate materials science that were presented at the UK China Particle Technology Forum III held at Birmingham, UK in 2011. Classified into four distinct topics namely synthesis, characterisation, processing and modelling, the chapters showcase the advances in these areas including a range of advanced synthesis methods for example, spray-pyrolysis, supercritical fluid synthesis assisted with ultrasound, continuous synthesis using supercritical water, hydrothermal synthesis of nano-particulate materials and jet milling. For characterisation, various methods for characterising particulate materials at both particle and system levels are introduced and how these properties affect the behaviour of particulate materials in various processes, such as inhalation, filling, and consolidation, are discussed. In the processing section, recent advances such as capsule filling, micro-dosing, dry granulation, roll compaction, milling, and more are presented. The last section concerns mathematical and numerical modelling in particulate materials, for which the book includes both analytical methods and advanced numerical methods, such as discrete element methods (DEM), computational fluid dynamics (CFD), lattice Boltzmann methods (LBM), coupled DEM/CFD and DEM/LBM, and their applications. Particulate Materials is aimed at research communities dealing with these diverse materials, and scientists and engineers in powder handling industries, such as pharmaceutical, food, fine chemical and detergents. "
Develop a deeper understanding of mathematical concepts and their applications with new and updated editions from our bestselling series. - Build connections between topics using real-world contexts that develop mathematical modelling skills, thus providing your students with a fuller and more coherent understanding of mathematical concepts. - Develop fluency in problem-solving, proof and modelling with plenty of questions and well-structured exercises. - Overcome misconceptions and develop mathematical insight with annotated worked examples. - Enhance understanding and map your progress with graduated exercises that support you at every stage of your learning.
You are an idiot. Don't get defensive! It's not your fault. For decades your teachers, authority figures and textbooks have been lying to you. You do not have five senses. Your tongue doesn't have neatly segregated taste-bud zones. You don't know what the pyramids really looked like. You're even pooping wrong - Jesus, you're a wreck! But it's going to be okay. Because we're here to help. Packed with more sexy facts than the Encyclopedia Pornographica, the Cracked De-Textbook will teach you about the true stars of history, why you picture everything from Velociraptors to Ancient Rome incorrectly, and finally, at long last - how to pop a proper squat. This book was built from the ground up to systematically seek out, dismantle and destroy the many untruths that years of misguided education have left festering inside of you, and leave you a smarter person...whether you like it or not. The De-Textbook is a merciless, brutal learning machine. It can't be bargained with. It can't be reasoned with. It doesn't feel pity, or remorse, or fear. And it absolutely will not stop, ever, until you are informed.
Build your knowledge and understanding with guidance and assessment preparation covering the Statistics options of the new AS and A-level specifications, from a team of subject experts and authors sourced from MEI. - Build reasoning and problem-solving skills with practice questions and well-structured exercises that improve statistical techniques. - Develop a fuller understanding of statistics concepts with real world examples that help build connections between topics and develop modelling skills. - Address misconceptions and develop problem-solving with annotated worked examples. - Supports you at every stage of your learning with graduated exercises that improve understanding and measure progress.
Mathematics of Computing -- General.
This series, well-known for accessibility and for a student-friendly approach, has a wealth of features: worked examples, activities, investigations, graded exercises, Key Points summaries and Discussion Points. To ensure exam success there are plenty of up-to-date exam questions, plus warning signs to indicate common pitfalls. MEI offer full support to schools through their network with newsletters, training days and an annual conference.
Simplify machine learning model implementations with Spark About This Book Solve the day-to-day problems of data science with Spark This unique cookbook consists of exciting and intuitive numerical recipes Optimize your work by acquiring, cleaning, analyzing, predicting, and visualizing your data Who This Book Is For This book is for Scala developers with a fairly good exposure to and understanding of machine learning techniques, but lack practical implementations with Spark. A solid knowledge of machine learning algorithms is assumed, as well as hands-on experience of implementing ML algorithms with Scala. However, you do not need to be acquainted with the Spark ML libraries and ecosystem. What You Will Learn Get to know how Scala and Spark go hand-in-hand for developers when developing ML systems with Spark Build a recommendation engine that scales with Spark Find out how to build unsupervised clustering systems to classify data in Spark Build machine learning systems with the Decision Tree and Ensemble models in Spark Deal with the curse of high-dimensionality in big data using Spark Implement Text analytics for Search Engines in Spark Streaming Machine Learning System implementation using Spark In Detail Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability, and optimization. Learning about algorithms enables a wide range of applications, from everyday tasks such as product recommendations and spam filtering to cutting edge applications such as self-driving cars and personalized medicine. You will gain hands-on experience of applying these principles using Apache Spark, a resilient cluster computing system well suited for large-scale machine learning tasks. This book begins with a quick overview of setting up the necessary IDEs to facilitate the execution of code examples that will be covered in various chapters. It also highlights some key issues developers face while working with machine learning algorithms on the Spark platform. We progress by uncovering the various Spark APIs and the implementation of ML algorithms with developing classification systems, recommendation engines, text analytics, clustering, and learning systems. Toward the final chapters, we'll focus on building high-end applications and explain various unsupervised methodologies and challenges to tackle when implementing with big data ML systems. Style and approach This book is packed with intuitive recipes supported with line-by-line explanations to help you understand how to optimize your work flow and resolve problems when working with complex data modeling tasks and predictive algorithms. This is a valuable resource for data scientists and those working on large scale data projects.
Accuracy and Stability of Numerical Algorithms gives a thorough, up-to-date treatment of the behavior of numerical algorithms in finite precision arithmetic. It combines algorithmic derivations, perturbation theory, and rounding error analysis, all enlivened by historical perspective and informative quotations. This second edition expands and updates the coverage of the first edition (1996) and includes numerous improvements to the original material. Two new chapters treat symmetric indefinite systems and skew-symmetric systems, and nonlinear systems and Newton's method. Twelve new sections include coverage of additional error bounds for Gaussian elimination, rank revealing LU factorizations, weighted and constrained least squares problems, and the fused multiply-add operation found on some modern computer architectures.
The book provides the reader with a multifaceted picture of mathematics education in Israel, put into an international perspective where relevant. It is intended to give an overview of a wide range of topics covering issues such as raising and maintaining motivation, search for excellence, treatment of difficulties, teacher education, language issues, minorities issues, curriculum changes over the first 70 years of the state of Israel, and many more. This includes aspects of research and practice into the teaching and learning of mathematics, innovation, developments, policy, achievements, and implementation with some international comparison as well. Contents: Issues and Innovations Related to the Structure of Mathematics Education in Israel: Highlights in the Development of Education and Mathematics Education in the State of Israel: A Timeline (Michael N Fried, Hannah Perl and Abraham Arcavi) How Did a Crisis in Mathematics Education Lead to a Positive Reform? (Muhana Fares) A Start-Up Nation at Risk: Israel's Quest for Excellence (Eli Hurvitz) Supervision of Mathematics Teaching by the Ministry of Education (Hannah Perl, Dorit Neria, Ruth Segal and Niza Sion) Mathematics Education in Israeli Religious High-Schools (Thierry (Noah) Dana-Picard and Sara Hershkovitz) Excellence in Mathematics in the Ultra-Orthodox Community: Fantasy or Reality? (Reuven Gal, Yehuda Morgenstern and Yael Elimelech) Mathematics Education in the Arabic-Speaking Sectors in Israel (Shaker A Rasslan and Amal Sharif-Rasslan) Issues and Innovations Related to Mathematics Education at Preschool and Primary School (Grades K-6) in Israel: New Developments and Trends in Preschool Mathematics Education in Israel (Ornit Spektor-Levy and Taly Shechter) Origametria — Paper Folding for Teaching Geometry in Preschool and Primary School (John Oberman) Educating the Eye: The Agam Program for Visual Thinking (Rina Hershkowitz, Zvia Markovits, Sherman Rosenfeld, Lea Ilani and Bat-Sheva Eylon) Professional Development for Preschool Teachers: The CAMTE Framework and Repeating Patterns (Dina Tirosh, Pessia Tsamir, Esther Levenson and Ruthi Barkai) Time to Know — A Socio-constructivist Initiative to Integrate Computers in the Teaching and Learning of Primary Mathematics (Dovi Weiss and Tali Wallach) Issues and Innovations Related to Mathematics Education at Middle and High School (Grades 7–12) in Israel: Exhausting Students' Potential in Mathematics: A Comprehensive Approach to Promoting Both Struggling and Promising Students (Orit Zaslavsky, Liora Linchevski, Noga Hermon, Drora Livneh and Iris Zodik) Middle School Mathematics Curriculum Based on the Power of Open Technological Tools: The Case of CompuMath Project (Rina Hershkowitz and Michal Tabach) Mathematics at the Virtual School: Why? Why not? Who? What? And So What? (Yaniv Biton, Osnat Fellus, Dafna Raviv, David Feilchenfeld and Boris Koichu) Nurturing Students with High Mathematical Potential (Abraham (Avi) Berman and Roza Leikin) The Bar-Ilan University — ICAMS Program for the Advancement of Mathematically Talented Youth (Zvi Arad and Elisheva (Gerstein) Fridman) Mathematical Excellence: The Mofet Way (Tamara Avissar-Zeldis) The Advancement of Mathematics Studies in the ORT Israel Educational Network — Policy and Implementation (Lea Dolev and Eli Eisenberg) Promoting Advanced-Level Mathematics in Diverse Populations in the Amal Educational Network (Ronit Ashkenazy and Anna Vaknin) Problem-Solving Forums on Social Networks that Accompany

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