Machine Learning Algorithms for Problem Solving in Computational Applications

This self-contained introduction to machine learning, designed from the start with engineers in mind, will equip students with everything they need to start applying machine learning principles and algorithms to real-world engineering problems. With a consistent emphasis on the connections between estimation, detection, information theory, and optimization, it includes: an accessible overview of the relationships between machine learning and signal processing, providing a solid foundation for further study; clear explanations of the differences between state-of-the-art techniques and more classical methods, equipping students with all the understanding they need to make informed technique choices; demonstration of the links between information-theoretical concepts and their practical engineering relevance; reproducible examples using Matlab, enabling hands-on student experimentation. Assuming only a basic understanding of probability and linear algebra, and accompanied by lecture slide

Fundamental theory and practical algorithms of weakly supervised classification, emphasizing an approach based on empirical risk minimization.Standard machine learning techniques require large amounts of labeled data to work well. When we apply machine learning to problems in the physical world, however, it is extremely difficult to collect such quantities of labeled data. This book presents theory and algorithms for weakly supervised learning, a paradigm of machine learning from weakly labeled data. Emphasizing an approach based on empirical risk minimization and drawing on state-of-the-art research in weakly supervised learning, the book provides both the fundamentals of the field and the advanced mathematical theories underlying them. It can be used as a reference for practitioners and researchers and in the classroom.The book first mathematically formulates classification problems, defines common notations, and reviews various algorithms for supervised binary and multiclass classif

A broad introduction to algorithms for decision making under uncertainty, introducing the underlying mathematical problem formulations and the algorithms for solving them.Automated decision-making systems or decision-support systems―used in applications that range from aircraft collision avoidance to breast cancer screening―must be designed to account for various sources of uncertainty while carefully balancing multiple objectives. This textbook provides a broad introduction to algorithms for decision making under uncertainty, covering the underlying mathematical problem formulations and the algorithms for solving them. The book first addresses the problem of reasoning about uncertainty and objectives in simple decisions at a single point in time, and then turns to sequential decision problems in stochastic environments where the outcomes of our actions are uncertain. It goes on to address model uncertainty, when we do not start with a known model and must learn how to act through inte

Probabilistic numerical computation formalises the connection between machine learning and applied mathematics. Numerical algorithms approximate intractable quantities from computable ones. They estimate integrals from evaluations of the integrand, or the path of a dynamical system described by differential equations from evaluations of the vector field. In other words, they infer a latent quantity from data. This book shows that it is thus formally possible to think of computational routines as learning machines, and to use the notion of Bayesian inference to build more flexible, efficient, or customised algorithms for computation. The text caters for Masters' and PhD students, as well as postgraduate researchers in artificial intelligence, computer science, statistics, and applied mathematics. Extensive background material is provided along with a wealth of figures, worked examples, and exercises (with solutions) to develop intuition.

In just 24 lessons of one hour or less, Design Thinking for Tech helps you inject techniques and exercises into your projects using the same systematic and creative process that designers have used for years. Anderson walks you through a simple four-phase Design Thinking model, showing how to loop back, keep learning, and continuously refine your work. You'll start by understanding the essential "what, how, when, why, and who" of Design Thinking. Next, you'll use core Design Thinking techniques to understand the big picture, focus on your most critical problems, think more creatively about them, take the next best steps toward problem resolution and value creation, and along the way rapidly iterate for progress. Every lesson builds on what you've already learned, with exercises crafted to deliver directly relevant experience. Regardless of your role in the world of technology, you'll learn how to supercharge success for any tech-related project, business initiative, or digital transfor

Techniques for applying mathematical concepts in the real world: six rarely taught but crucial tools for analysis, research, and problem-solving.Many young graduates leave school with a solid knowledge of mathematical concepts but struggle to apply these concepts in practice. Real scientific and engineering problems are different from those found in textbooks: they are messier, take longer to solve, and standard solution recipes might not apply. This book fills the gap between what is taught in the typical college curriculum and what a practicing engineer or scientist needs to know. It presents six powerful tools for analysis, research, and problem-solving in the real world: dimensional analysis, limiting cases, symmetry, scaling, making order of magnitude estimates, and the method of successive approximations. The book does not focus on formulaic manipulations of equations, but emphasizes analysis and explores connections between the equations and the application. Each chapter introdu

A guide to computational thinking education, with a focus on artificial intelligence literacy and the integration of computing and physical objects. Computing has become an essential part of today’s primary and secondary school curricula. In recent years, K–12 computer education has shifted from computer science itself to the broader perspective of computational thinking (CT), which is less about technology than a way of thinking and solving problems―“a fundamental skill for everyone, not just computer scientists,” in the words of Jeanette Wing, author of a foundational article on CT. This volume introduces a variety of approaches to CT in K–12 education, offering a wide range of international perspectives that focus on artificial intelligence (AI) literacy and the integration of computing and physical objects. The book first offers an overview of CT and its importance in K–12 education, covering such topics as the rationale for teaching CT; programming as a general problem-solving

A comprehensive update of the leading algorithms text, with new material on matchings in bipartite graphs, online algorithms, machine learning, and other topics. Some books on algorithms are rigorous but incomplete; others cover masses of material but lack rigor. Introduction to Algorithms uniquely combines rigor and comprehensiveness. It covers a broad range of algorithms in depth, yet makes their design and analysis accessible to all levels of readers, with self-contained chapters and algorithms in pseudocode. Since the publication of the first edition, Introduction to Algorithms has become the leading algorithms text in universities worldwide as well as the standard reference for professionals. This fourth edition has been updated throughout. New for the fourth edition New chapters on matchings in bipartite graphs, online algorithms, and machine learningNew material on topics including solving recurrence equations, hash tables, potential functions, and suffix arrays140 new exerc

Recovering the phase of the Fourier transform is a ubiquitous problem in imaging applications from astronomy to nanoscale X-ray diffraction imaging. Despite the efforts of a multitude of scientists, from astronomers to mathematicians, there is, as yet, no satisfactory theoretical or algorithmic solution to this class of problems. Written for mathematicians, physicists and engineers working in image analysis and reconstruction, this book introduces a conceptual, geometric framework for the analysis of these problems, leading to a deeper understanding of the essential, algorithmically independent, difficulty of their solutions. Using this framework, the book studies standard algorithms and a range of theoretical issues in phase retrieval and provides several new algorithms and approaches to this problem with the potential to improve the reconstructed images. The book is lavishly illustrated with the results of numerous numerical experiments that motivate the theoretical development and p

The book elucidates the revolutionary and riveting research in the ultramodern domain of drone technologies, drone-enabled IoT applications, and artificial intelligence-based smart surveillance. The book explains the most recent developments in the field, challenges, and future scope of drone technologies. Beyond that, it discusses the importance of a wide range of design applications, drone/UAV development, and drone-enabled smart healthcare systems for smart cities. It describes pioneering work on mitigating cyber security threats by employing intelligent machine learning models in the designing of IoT-aided drones. The book also has a fascinating chapter on application intrusion detection by drones using recurrent neural networks. Other chapters address interdisciplinary fields like artificial intelligence, deep learning, the role of drones in healthcare in smart cities, and the importance of drone technology in agriculture.

Press-out the pieces and explore the farm with Let's Learn & Play! On the Farm from Priddy Books. This fantastic new novelty format is aimed at children aged three and over. Each of the illustrated pages is full of things for children to spot and look for as they learn about farm life from feeding the animals to shearing sheep, milking the cows and more. Packaged in a sturdy case with a magnetic fastening, there is a board book with die-cut spaces to fit in over 20 play pieces to complete the busy scenes. There are prompts on the page, as well as word labels to help children work out where to put the pieces which is a great way of encouraging hand-eye coordination, fine motor skills and problem solving. Each spread also includes simple fun facts about farms to inspire learning with different farm processes to find out about and encourage further discovery. This is a great gift as the pieces can be stored back in the clamshell and taken out and about. Perfect for entertaining on the

In recent years the development of new classification and regression algorithms based on deep learning has led to a revolution in the fields of artificial intelligence, machine learning, and data analysis. The development of a theoretical foundation to guarantee the success of these algorithms constitutes one of the most active and exciting research topics in applied mathematics. This book presents the current mathematical understanding of deep learning methods from the point of view of the leading experts in the field. It serves as both a starting point for researchers and graduate students in mathematics trying to get into the field, as well as an invaluable reference for future research.

Encourage students to develop collaborative problem-solving skills with STEAM Project-Based Learning! The engaging STEAM tasks provide meaningful learning opportunities for students as they work together to study current real-world problems and strategize solutions to help create a better world. These complete units include in-depth activities that provide important context for students as they learn about each topic before they develop solutions. Each robust unit includes a hands-on STEAM task that provides students with a problem to solve using the design process of Problem and Task, Research, Brainstorm and Design, Make It and Explain It. Additional resources in each unit include: - Teacher Page with a unit overview and implementation suggestions - Real-World Connection that introduces the topic with a fictional story - What Is the Problem? page to help students brainstorm solutions to the issue - Learn About the Problem page that provides detailed information about the problem in t

Artificial Intelligence-Aided Materials Design: AI-Algorithms and Case Studies on Alloys and Metallurgical Processes describes the application of artificial intelligence (AI)/machine learning (ML) concepts to develop predictive models that can be used to design alloy materials, including magnetic alloys, nickel-base superalloys, titanium-base alloys, and aluminum-base alloys. Readers new to AI/ML algorithms can use this book as a starting point and use the included MATLAB and Python implementation of AI/ML algorithms through included case studies. Experienced AI/ML researchers who want to try new algorithms can use this book and study the case studies for reference. Offers advantages and limitations of several AI concepts and their proper implementation in various data types generated through experiments and computer simulations and from industries in different file formats Helps readers develop predictive models through AI/ML algorithms by writing their own computer code or usin

Give your first grader a strong computer science foundation with Smart Start: Beginning Coding Stories and Activities. Children will learn important coding concepts with engaging stories and practice activities. Based on the K-12 Computer Science Framework, this dynamic early learning series helps children understand coding concepts to develop a strong foundation in computer science, including computational thinking, problem solving, and critical thinking. Each full-color activity book is organized into eight themed units around one coding concept and includes: - Coding stories that demonstrate what coders do and include an audio read-aloud feature - Concept pages that connect coding concepts to real-world situations - Practice activities that reinforce important coding rules - Review pages for every coding concept practiced - Coding projects with print-based games and puzzles - Mini coding handbook with key terms Computer science concepts and skills help early learners become critical

Give your kindergartner a strong computer science foundation with Smart Start: Beginning Coding Stories and Activities. Children will learn important coding concepts with engaging stories and practice activities. Based on the K-12 Computer Science Framework, this dynamic early learning series helps children understand coding concepts to develop a strong foundation in computer science, including computational thinking, problem solving, and critical thinking. Each full-color activity book is organized into eight themed units around one coding concept and includes: - Coding stories that demonstrate what coders do and include an audio read-aloud feature - Concept pages that connect coding concepts to real-world situations - Practice activities that reinforce important coding rules - Review pages for every coding concept practiced - Coding projects with print-based games and puzzles - Mini coding handbook with key terms Computer science concepts and skills help early learners become critica

Give your preschooler a strong computer science foundation with Smart Start: Beginning Coding Stories and Activities. Children will learn important coding concepts with engaging stories and practice activities. Based on the K-12 Computer Science Framework, this dynamic early learning series helps children understand coding concepts to develop a strong foundation in computer science, including computational thinking, problem solving, and critical thinking. Each full-color activity book is organized into eight themed units around one coding concept and includes: - Coding stories that demonstrate what coders do and include an audio read-aloud feature - Concept pages that connect coding concepts to real-world situations - Practice activities that reinforce important coding rules - Review pages for every coding concept practiced - Coding projects with print-based games and puzzles - Mini coding handbook with key terms Computer science concepts and skills help early learners become critical

Finance students and practitioners may ask: can machines learn everything? Could AI help me? Computing students or practitioners may ask: which of my skills could contribute to finance? Where in finance should I pay attention? This book aims to answer these questions. No prior knowledge is expected in AI or finance.Including original research, the book explains the impact of ignoring computation in classical economics; examines the relationship between computing and finance and points out potential misunderstandings between economists and computer scientists; and introduces Directional Change and explains how this can be used.To finance students and practitioners, this book will explain the promise of AI, as well as its limitations. It will cover knowledge representation, modelling, simulation and machine learning, explaining the principles of how they work. To computing students and practitioners, this book will introduce the financial applications in which AI has made an impact. This

No magical creature has a problem too big for Ava and her Dads to solve!Unicorns with tummy trouble. Dragons with broken wings. Sneezy centaurs and mermaids with measles.Everyone in town knows just who to see when they are feeling poorly: Ava and her dads, the Marvellous Doctors for Magical Creatures. Well, Ava isn't a doctor, not yet, but she's learning the ropes and she's brilliant at solving medical mysteries. Glitterbug the unicorn is not like all the other unicorns.They love dancing and planning parties, but she would much rather be frolicking in the mud. Glitterbug comes to the Marvellous Doctors with a terrible tummy ache, but after examining her, they are mystified as to what's causing it. Can Ava help her dads find the answer?In this magical story about being true to yourself, Ava discovers there's more than one way to be a unicorn!

Introduce your little one to the wonders of nature with 100+ outdoor learning activities!Give your child all the benefits of nature with this activity book designed for children ages 2-5. Whether you’re planning an afternoon in a local park, a day in the backyard, or a morning hike in a nature preserve, you and your child will find more than 100 ideas in these pages for observing, exploring, creating, and playing in nature.Nature Activity Book for Little Ones features: 100+ outdoor activities for kids ages 2-5, geared toward their interests and developmental milestones—with little or no preparation requiredBonus activities for additional learning, including numbers, letters, shapes, and colorsSchool prep: the activities teach problem-solving, communication, and responsibility, important skills for school succesLess screen time and more nature time together to fuel your child’s curiosity and creativity and help them build an early connection with natureBest of all, this entertaining boo

The dramatic increase in computer performance has been extraordinary, but not for all computations: it has key limits and structure. Software architects, developers, and even data scientists need to understand how exploit the fundamental structure of computer performance to harness it for future applications. Ideal for upper level undergraduates, Computer Architecture for Scientists covers four key pillars of computer performance and imparts a high-level basis for reasoning with and understanding these concepts: Small is fast – how size scaling drives performance; Implicit parallelism – how a sequential program can be executed faster with parallelism; Dynamic locality – skirting physical limits, by arranging data in a smaller space; Parallelism – increasing performance with teams of workers. These principles and models provide approachable high-level insights and quantitative modelling without distracting low-level detail. Finally, the text covers the GPU and machine-learning accelerat

This book describes how computer programs can generate narratives and how studies of computational narrative can illuminate how humans tell stories. Combining an introduction to relevant concepts related to automatic storytelling with accessible descriptions of well-known computer programs that illustrate how such concepts are employed, the book is aimed at an interdisciplinary audience and assumes little or no background in computer science. The book introduces the most relevant techniques employed over the last 60 years for the development of computer models for narrative generation, including narrative templates, problem-solving, planning, author engagement and reflection, and statistical methods such as deep neural networks, ending with an examination of the societal implications of the development of automatic narrative generator systems.