Demystifying Computation

Problem solving in computing is referred to as computational thinking. The theory behind this concept is challenging in its technicalities, yet simple in its ideas. This book introduces the theory of computation from its inception to current form of complexity; from explanations of how the field of computer science was formed using classical ideas in mathematics by Gödel, to conceptualization of the Turing Machine, to its more recent innovations in quantum computation, hypercomputation, vague computing and natural computing. It describes the impact of these in relation to academia, business and wider society, providing a sound theoretical basis for its practical application. Written for accessibility, Demystifying Computation provides the basic knowledge needed for non-experts in the field, undergraduate computer scientists and students of information and communication technology and software development. Request Inspection Copy Contents:A Brief History of ComputingFrom Hilbert to Gödel to TuringHypercomputationNatural ComputingQuantum ComputingVague ComputingPhysical Reality and Computation Readership: High-School and undergraduate students in computer science, information and communication technology, and software development, and non-experts in the field looking to understand how computation works.

This book presents an emerging new vision of the brain, which is essentially expressed in computational terms, for non-experts. As such, it presents the fundamental concepts of neuroscience in simple language, without overwhelming non-biologists with excessive biological jargon. In addition, the book presents a novel computational perspective on the brain for biologists, without resorting to complex mathematical equations. It addresses a comprehensive range of topics, starting with the history of neuroscience, the function of the individual neuron, the various kinds of neural network models that can explain diverse neural phenomena, sensory-motor function, language, emotions, and concluding with the latest theories on consciousness. The book offers readers a panoramic introduction to the “new brain” and a valuable resource for interdisciplinary researchers looking to gatecrash the world of neuroscience.

Geometric Computation: Foundations for Design describes the mathematical and computational concepts that are central to the practical application of design computation in a manner tailored to the visual designer. Uniquely pairing key topics in code and geometry, this book develops the two key faculties required by designers that seek to integrate computation into their creative practice: an understanding of the structure of code in object-oriented programming, and a proficiency in the fundamental geometric constructs that underlie much of the computational media in visual design.

Offers fundamental theories and practical and more sophisticated applications of Evolutionary Computation in varied industries Provides insight into various platforms, paradigms, techniques, and tools used in Evolutionary Computation for diverse fields Presents an understanding related to optimization, performance tuning, virtualization, deployment models, and their applications Covers a variety of applications for social and essential models and is based on real life examples Useful for decision making based on optimized data through Evolutionary Computation in multi-dimensions

This book presents different approaches for answering the question: How do we assess computational thinking? The result is a snapshot of the current state of the field for assessing computational thinking. The last decade has seen rapid growth in the presence of computational thinking (CT) in educational contexts. Those working to advance CT argue that the concepts and skills associated with CT are essential to succeed in an increasingly computational world. As a result of these efforts, there has been tremendous growth in curricula, learning environments, and innovations around CT education in K-12 classrooms and beyond. As CT grows in prominence, so too does the need to be able to effectively and equitably assess learners CT abilities. This volume is a collection of chapters pursuing different approaches for answering the question: How do we assess computational thinking? The answers provided span age ranges, formal and informal contexts, conceptual aspects of CT, and varying methodological and evaluative strategies. Collectively, the volume captures the current state of the field for assessing computational thinking and lays the groundwork for future CT assessment innovation. Assessing Computational Thinking will be a key resource for academics, researchers, and advanced students of Education, Educational Assessment, Educational Research, Psychology and Research Methods. The chapters included in this book were originally published as a special issue of Computer Science Education.

Computational technologies have been impacting human life for years. Teaching methods must adapt accordingly to provide the next generation with the necessary knowledge to further advance these human-assistive technologies. Teaching Computational Thinking in Primary Education is a crucial resource that examines the impact that instructing with a computational focus can have on future learners. Highlighting relevant topics that include multifaceted skillsets, coding, programming methods, and digital games, this scholarly publication is ideal for educators, academicians, students, and researchers who are interested in discovering how the future of education is being shaped.

This book reports on research and practice on computational thinking and the effect it is having on education worldwide, both inside and outside of formal schooling. With coding becoming a required skill in an increasing number of national curricula (e.g., the United Kingdom, Israel, Estonia, Finland), the ability to think computationally is quickly becoming a primary 21st century “basic” domain of knowledge. The authors of this book investigate how this skill can be taught and its resultant effects on learning throughout a student's education, from elementary school to adult learning.

Focuses on new machine learning developments that can lead to newly developed applications Uses a predictive and futuristic approach which makes Machine Learning a promising tool for business processes and sustainable solutions Promotes newer algorithms which are more efficient and reliable for a new dimension in discovering certain latent domains of applications Discusses the huge potential in making better use of machines in order to ensure optimal prediction, execution, and decision-making Offers many real-time case studies