Efficiency in Learning offers a road map of the most effective ways to use the three fundamental communication of training: visuals, written text, and audio. Regardless of how you are delivering your training materials—in the classroom, in print, by synchronous or asynchronous media—the book’s methods are easily applied to your lesson presentations, handouts, reference guides, or e-learning screens. Designed to be a down-to-earth resource for all instructional professionals, Efficiency in Learning’s guidelines are clearly illustrated with real-world examples.
Machine learning techniques provide cost-effective alternatives to traditional methods for extracting underlying relationships between information and data and for predicting future events by processing existing information to train models. Efficient Learning Machines explores the major topics of machine learning, including knowledge discovery, classifications, genetic algorithms, neural networking, kernel methods, and biologically-inspired techniques. Mariette Awad and Rahul Khanna’s synthetic approach weaves together the theoretical exposition, design principles, and practical applications of efficient machine learning. Their experiential emphasis, expressed in their close analysis of sample algorithms throughout the book, aims to equip engineers, students of engineering, and system designers to design and create new and more efficient machine learning systems. Readers of Efficient Learning Machines will learn how to recognize and analyze the problems that machine learning technology can solve for them, how to implement and deploy standard solutions to sample problems, and how to design new systems and solutions. Advances in computing performance, storage, memory, unstructured information retrieval, and cloud computing have coevolved with a new generation of machine learning paradigms and big data analytics, which the authors present in the conceptual context of their traditional precursors. Awad and Khanna explore current developments in the deep learning techniques of deep neural networks, hierarchical temporal memory, and cortical algorithms. Nature suggests sophisticated learning techniques that deploy simple rules to generate highly intelligent and organized behaviors with adaptive, evolutionary, and distributed properties. The authors examine the most popular biologically-inspired algorithms, together with a sample application to distributed datacenter management. They also discuss machine learning techniques for addressing problems of multi-objective optimization in which solutions in real-world systems are constrained and evaluated based on how well they perform with respect to multiple objectives in aggregate. Two chapters on support vector machines and their extensions focus on recent improvements to the classification and regression techniques at the core of machine learning.
This handy resource describes and illustrates the concepts underlying the “First Principles of Instruction” and illustrates First Principles and their application in a wide variety of instructional products. The book introduces the e3 Course Critique Checklist that can be used to evaluate existing instructional product. It also provides directions for applying this checklist and illustrates its use for a variety of different kinds of courses. The Author has also developed a Pebble-in-the-Pond instructional design model with an accompanying e3 ID Checklist. This checklist enables instructional designers to design and develop instructional products that more adequately implement First Principles of Instruction.
To most of us, learning something "the hard way" implies wasted time and effort. Good teaching, we believe, should be creatively tailored to the different learning styles of students and should use strategies that make learning easier. Make It Stick turns fashionable ideas like these on their head. Drawing on recent discoveries in cognitive psychology and other disciplines, the authors offer concrete techniques for becoming more productive learners. Memory plays a central role in our ability to carry out complex cognitive tasks, such as applying knowledge to problems never before encountered and drawing inferences from facts already known. New insights into how memory is encoded, consolidated, and later retrieved have led to a better understanding of how we learn. Grappling with the impediments that make learning challenging leads both to more complex mastery and better retention of what was learned. Many common study habits and practice routines turn out to be counterproductive. Underlining and highlighting, rereading, cramming, and single-minded repetition of new skills create the illusion of mastery, but gains fade quickly. More complex and durable learning come from self-testing, introducing certain difficulties in practice, waiting to re-study new material until a little forgetting has set in, and interleaving the practice of one skill or topic with another. Speaking most urgently to students, teachers, trainers, and athletes, Make It Stick will appeal to all those interested in the challenge of lifelong learning and self-improvement.
"A compendium of empirically verified instructional methods derived from research in behavioral analysis. Coverage includes precision teaching, direct instruction, computerized teaching, and personalized system of instruction, as well as discussing the use of peer tutoring, and chapters specific to teaching language, cognition, grammar and writing"--Book jacket.
Computational modeling and simulation has developed and expanded into a diverse range of fields such as digital signal processing, image processing, robotics, systems biology, and many more; enhancing the need for a diversifying problem solving applications in this area. Efficiency and Scalability Methods for Computational Intellect presents various theories and methods for approaching the problem of modeling and simulating intellect in order to target computation efficiency and scalability of proposed methods. Researchers, instructors, and graduate students will benefit from this current research and will in turn be able to apply the knowledge in an effective manner to gain an understanding of how to improve this field.
Industrial policy, once relegated to resource allocation, technological improvements, and the modernization of industries, should be treated as a serious component of sustainability and developmental economics. A rich set of complimentary institutions, shared behavioral norms, and public policies have sustained economic growth from Britain's industrial revolution onwards. This volume revisits the role of industrial policy in the success of these strategies and what it can offer developed and developing economies today. Featuring essays from experts invested in the expansion of industrial policies, topics discussed include the most effective use of industrial policies in learning economies, development finance, and promoting investment in regional and global contexts. Also included are in-depth case studies of Japan and India's experience with industrial policy in the banking and private sector. One essay revisits the theoretical and conceptual foundations of industrial policy from a structural economics perspective and another describes the models, packages, and transformation cycles that constitute a variety of approaches to implementation. The collection concludes with industrial strategies for facilitating quality growth, realizing more sustainable manufacturing development, and encouraging countries to industrialize around their natural resources.
A bold challenge to our obsession with efficiency—and a new understanding of how to benefit from the powerful potential of serendipity. Algorithms, multitasking, the sharing economy, life hacks: our culture can't get enough of efficiency. One of the great promises of the Internet and big data revolutions is the idea that we can improve the processes and routines of our work and personal lives to get more done in less time than we ever have before. There is no doubt that we're performing at higher levels and moving at unprecedented speed, but what if we're headed in the wrong direction? Melding the long-term history of technology with the latest headlines and findings of computer science and social science, The Efficiency Paradox questions our ingrained assumptions about efficiency, persuasively showing how relying on the algorithms of digital platforms can in fact lead to wasted efforts, missed opportunities, and, above all, an inability to break out of established patterns. Edward Tenner offers a smarter way of thinking about efficiency, revealing what we and our institutions, when equipped with an astute combination of artificial intelligence and trained intuition, can learn from the random and unexpected.
Training is both a teaching and a learning experience, and just about everyone has had that experience. Training involves acquiring knowledge and skills. This newly acquired training information is meant to be applicable to specific activities, tasks, and jobs. In modern times, where jobs are increasingly more complex, training workers to perform successfully is of more importance than ever. The range of contexts in which training is required includes industrial, corporate, military, artistic, and sporting, at all levels from assembly line to executive function. The required training can take place in a variety of ways and settings, including the classroom, the laboratory, the studio, the playing field, and the work environment itself. The general goal of this book is to describe the current state of research on training using cognitive psychology to build a complete empirical and theoretical picture of the training process. The book focuses on training cognition, as opposed to physical or fitness training. It attempts to show how to optimize training efficiency, durability, and generalizability. The book includes a review of relevant cognitive psychological literature, a summary of recent laboratory experiments, a presentation of original theoretical ideas, and a discussion of possible applications to real-world training settings.
