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Author: J. E. R. Staddon
Publisher: Cambridge University Press
Published: 2016-03-10
Total Pages: 619
ISBN-13: 1107082471
DOWNLOAD EBOOK →Summarizes the current state of both theoretical and experimental knowledge about learning in animals.
Author: Aude Billard
Publisher: MIT Press
Published: 2022-02-08
Total Pages: 425
ISBN-13: 0262367017
DOWNLOAD EBOOK →Methods by which robots can learn control laws that enable real-time reactivity using dynamical systems; with applications and exercises. This book presents a wealth of machine learning techniques to make the control of robots more flexible and safe when interacting with humans. It introduces a set of control laws that enable reactivity using dynamical systems, a widely used method for solving motion-planning problems in robotics. These control approaches can replan in milliseconds to adapt to new environmental constraints and offer safe and compliant control of forces in contact. The techniques offer theoretical advantages, including convergence to a goal, non-penetration of obstacles, and passivity. The coverage of learning begins with low-level control parameters and progresses to higher-level competencies composed of combinations of skills. Learning for Adaptive and Reactive Robot Control is designed for graduate-level courses in robotics, with chapters that proceed from fundamentals to more advanced content. Techniques covered include learning from demonstration, optimization, and reinforcement learning, and using dynamical systems in learning control laws, trajectory planning, and methods for compliant and force control . Features for teaching in each chapter: applications, which range from arm manipulators to whole-body control of humanoid robots; pencil-and-paper and programming exercises; lecture videos, slides, and MATLAB code examples available on the author’s website . an eTextbook platform website offering protected material[EPS2] for instructors including solutions.
Author: Kinshuk
Publisher: Routledge
Published: 2016-03-31
Total Pages: 172
ISBN-13: 1317747704
DOWNLOAD EBOOK →Designing Adaptive and Personalized Learning Environments provides a theoretically-based yet practical guide to systematic design processes for learning environments that provide automatic customization of learning and instruction. The book consists of four main sections: In "Introduction and Overview," the concepts of adaptivity and personalization are introduced and explored in detail. In "Theoretical Perspectives with Example Applications," various theoretical concepts underlying adaptive and personalized learning are discussed, including cognitive profiling, content-based adaptivity, exploration-based adaptivity, and mobile and ubiquitous settings. In "Practical Perspectives with Example Applications," the implementation process for adaptive and personalized learning environments is described, followed by application in various contexts. In "Validation and Future Trends," various evaluation techniques for validating the efficiency and efficacy of adaptive and personalized learning systems are discussed. This final section concludes with a discussion of emerging trends in adaptive and personalized learning research. Based on cutting-edge research, Designing Adaptive and Personalized Learning Environments is appropriate as a primary textbook for both undergraduate and graduate courses focused on the design of learning systems, and as a secondary textbook for a variety of courses in programs such as educational technology, instructional design, learning sciences, digital literacy, computer based systems, and STEM content fields.
Author: Tadlaoui, Mouenis Anouar
Publisher: IGI Global
Published: 2019-12-13
Total Pages: 348
ISBN-13: 1799814947
DOWNLOAD EBOOK →As part of e-learning, adaptive systems are more specialized and focus on the adaptation of learning content and presentation of this content. An adaptive system focuses on how knowledge is learned and pays attention to the activities, cognitive structures, and context of the learning material. The adaptive term refers to the automatic adaptation of the system to the learner. The needs of the learner are borne by the system itself. The learner did not ask to change the parameters of the system to his own needs; it is rather the needs of the learner that will be supposed by the system. The system adapts according to this necessity. Personalization and Collaboration in Adaptive E-Learning is an essential reference book that aims to describe the specific steps in designing a scenario for a collaborative learning activity in the particular context of personalization in adaptive systems and the key decisions that need to be made by the teacher-learner. By applying theoretical and practical aspects of personalization in adaptive systems and applications within education, this collection features coverage on a broad range of topics that include adaptive teaching, personalized learning, and instructional design. This book is ideally designed for instructional designers, curriculum developers, educational software developers, IT specialists, educational administrators, professionals, professors, researchers, and students seeking current research on comparative studies and the pedagogical issues of personalized and collaborative learning.
Author: David Hung
Publisher: Springer Science & Business Media
Published: 2013-11-19
Total Pages: 326
ISBN-13: 9814560170
DOWNLOAD EBOOK →This volume introduces the concept of ‘adaptivity’ as occurring when, say, individuals cross boundaries. Through illustrations from both formal and informal learning, the book seeks to provide learning designs and frameworks for adaptivity. This book is unique as it ties together: a) social-individual dialectics; and b) adaptive learning as it relates to creativity and imagination. It highlights case studies from social / new media contexts, school learning milieux, and formal and informal situations. It approaches adaptive learning from the perspectives of students, teachers, school leaders, and participants in social media and other digitally mediated environments. The book is a valuable resource for practitioners and academics who are interested in adaptivity as a learning disposition.
Author: Ian Goodfellow
Publisher: MIT Press
Published: 2016-11-10
Total Pages: 801
ISBN-13: 0262337371
DOWNLOAD EBOOK →An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
Author: William Cutrer
Publisher: Elsevier Health Sciences
Published: 2019-09-29
Total Pages: 242
ISBN-13: 032371112X
DOWNLOAD EBOOK →Tomorrow’s best physicians will be those who continually learn, adjust, and innovate as new information and best practices evolve, reflecting adaptive expertise in response to practice challenges. As the first volume in the American Medical Association’s MedEd Innovation Series, The Master Adaptive Learner is an instructor-focused guide covering models for how to train and teach future clinicians who need to develop these adaptive skills and utilize them throughout their careers. Explains and clarifies the concept of a Master Adaptive Learner: a metacognitive approach to learning based on self-regulation that fosters the success and use of adaptive expertise in practice. Contains both theoretical and practical material for instructors and administrators, including guidance on how to implement a Master Adaptive Learner approach in today’s institutions. Gives instructors the tools needed to empower students to become efficient and successful adaptive learners. Helps medical faculty and instructors address gaps in physician training and prepare new doctors to practice effectively in 21st century healthcare systems. One of the American Medical Association Change MedEd initiatives and innovations, written and edited by members of the ACE (Accelerating Change in Medical Education) Consortium – a unique, innovative collaborative that allows for the sharing and dissemination of groundbreaking ideas and projects.
Author: Reimer Kühn
Publisher: Springer Science & Business Media
Published: 2013-06-29
Total Pages: 400
ISBN-13: 3662055945
DOWNLOAD EBOOK →Adaptivity and learning have in recent decades become a common concern of scientific disciplines. These issues have arisen in mathematics, physics, biology, informatics, economics, and other fields more or less simultaneously. The aim of this publication is the interdisciplinary discourse on the phenomenon of learning and adaptivity. Different perspectives are presented and compared to find fruitful concepts for the disciplines involved. The authors select problems showing representative traits concerning the frame up, the methods and the achievements rather than to present extended overviews.
Author: Geng Sun (Researcher on educational technology)
Publisher: World Scientific
Published: 2020
Total Pages: 151
ISBN-13: 9811207461
DOWNLOAD EBOOK →Author: Richard S. Sutton
Publisher: MIT Press
Published: 2018-11-13
Total Pages: 549
ISBN-13: 0262352702
DOWNLOAD EBOOK →The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.
