Learning by experience is both a part of the daily life and the exploration life. A systematic review of the past is essential to improve the exploration process by better managing risks and uncertainties. Learning through different disciplines has become a favoured technique. With new tools for interpretation and simulation, integration and data and the creation of cross-discipline teams, we can take major step forward in understanding the exploration task and its different elements.Global views and lessons learned on the Norwegian Continental Shelf on risk management and retrospective prospect assessment are presented in this book. Detailed exploration case histories from the Norwegian Continental Shelf documenting both positive and negative experiences and highlighting the benefits of integrated thinking and methods are presented. The impact of the application of various state-of-the art and developing technologies on portfolio management, opportunity evaluation and volumetric and risk assessment of prospects and discoveries are reviewed, and the future technological challenges in exploring the remaining hydrocarbon potential of the Norwegian continental Shelf are summarised.

The ability to learn is one of the most fundamental attributes of intelligent behavior. Consequently, progress in the theory and computer modeling of learn ing processes is of great significance to fields concerned with understanding in telligence. Such fields include cognitive science, artificial intelligence, infor mation science, pattern recognition, psychology, education, epistemology, philosophy, and related disciplines. The recent observance of the silver anniversary of artificial intelligence has been heralded by a surge of interest in machine learning-both in building models of human learning and in understanding how machines might be endowed with the ability to learn. This renewed interest has spawned many new research projects and resulted in an increase in related scientific activities. In the summer of 1980, the First Machine Learning Workshop was held at Carnegie-Mellon University in Pittsburgh. In the same year, three consecutive issues of the Inter national Journal of Policy Analysis and Information Systems were specially devoted to machine learning (No. 2, 3 and 4, 1980). In the spring of 1981, a special issue of the SIGART Newsletter No. 76 reviewed current research projects in the field. . This book contains tutorial overviews and research papers representative of contemporary trends in the area of machine learning as viewed from an artificial intelligence perspective. As the first available text on this subject, it is intended to fulfill several needs.

In some hands, history can be an inspirational and rewarding subject, yet in others it can seem dry and of little relevance. The aim of this textbook is to enable student teachers to learn to teach history in a way that pupils will find interesting, enjoyable and purposeful. It incorporates a wide range of ideas about the teaching of history with practical suggestions for classroom practice. This is the third edition of a textbook that has established itself as the leading text for student teachers of history. It has been thoroughly updated, with a revised chapter on the use of ICT in history teaching and major new sections in the areas of inclusion, resources, assessment and professional development. It provides an array of references and materials that give a sound theoretical foundation for the teaching of history, including weblinks to further resources. A range of tasks enable students to put their learning into practice in the classroom. The book also provides reference and access to a wide range of recent and relevant research in the field of history education, which will be of use to student teachers pursuing courses that have a Masters Level component. In all, it is an invaluable resource for student and beginning history teachers.

Deep reinforcement learning has attracted considerable attention recently. Impressive results have been achieved in such diverse fields as autonomous driving, game playing, molecular recombination, and robotics. In all these fields, computer programs have taught themselves to understand problems that were previously considered to be very difficult. In the game of Go, the program AlphaGo has even learned to outmatch three of the world’s leading players.Deep reinforcement learning takes its inspiration from the fields of biology and psychology. Biology has inspired the creation of artificial neural networks and deep learning, while psychology studies how animals and humans learn, and how subjects’ desired behavior can be reinforced with positive and negative stimuli. When we see how reinforcement learning teaches a simulated robot to walk, we are reminded of how children learn, through playful exploration. Techniques that are inspired by biology and psychology work amazingly well in computers: animal behavior and the structure of the brain as new blueprints for science and engineering. In fact, computers truly seem to possess aspects of human behavior; as such, this field goes to the heart of the dream of artificial intelligence. These research advances have not gone unnoticed by educators. Many universities have begun offering courses on the subject of deep reinforcement learning. The aim of this book is to provide an overview of the field, at the proper level of detail for a graduate course in artificial intelligence. It covers the complete field, from the basic algorithms of Deep Q-learning, to advanced topics such as multi-agent reinforcement learning and meta learning.

First released in the Spring of 1999, How People Learn has been expanded to show how the theories and insights from the original book can translate into actions and practice, now making a real connection between classroom activities and learning behavior. This edition includes far-reaching suggestions for research that could increase the impact that classroom teaching has on actual learning. Like the original edition, this book offers exciting new research about the mind and the brain that provides answers to a number of compelling questions. When do infants begin to learn? How do experts learn and how is this different from non-experts? What can teachers and schools do-with curricula, classroom settings, and teaching methodsâ€"to help children learn most effectively? New evidence from many branches of science has significantly added to our understanding of what it means to know, from the neural processes that occur during learning to the influence of culture on what people see and absorb. How People Learn examines these findings and their implications for what we teach, how we teach it, and how we assess what our children learn. The book uses exemplary teaching to illustrate how approaches based on what we now know result in in-depth learning. This new knowledge calls into question concepts and practices firmly entrenched in our current education system. Topics include: How learning actually changes the physical structure of the brain. How existing knowledge affects what people notice and how they learn. What the thought processes of experts tell us about how to teach. The amazing learning potential of infants. The relationship of classroom learning and everyday settings of community and workplace. Learning needs and opportunities for teachers. A realistic look at the role of technology in education.

Information Technology skill standards provide a common language for industry and education. It provides increased portability depending on attitude and performance of the professionals. The industry recognizes IT education programs that build competency among the students to perform the best in the new emerging trends in Information Technology. like Human Computer Interactions, Biometrics, Bioinformatics, Signal Processing. So this conference is organized to bring together leading academicians, industry experts and researchers in the area of emerging trends in Information Technology and facilitate personal interaction and discussions on various aspects of Information Technology. It also aims to provide a platform for the post-graduate students and research students to express their views about the emerging trends in Information Technology with interaction and exchange of ideas among the researchers and students from allover India. With this focus Technical/research papers are invitedfrom the students of MCA/ M.Sc (CS) / M.Sc.(IT)/ MCM and research students on the following topics. Biometrics Data Communication and Security Digital Image and Image Processing Human Computer Interaction Internet Technologies and Service Oriented Architecture Artificial Intelligence and Its Applications

Drawing on a wide range of international research, reflections and experiences of univeristy historians, this book links theory and practice and examines how high quality history teaching and learning can be acheived today in universities world wide.

Description of the Product: 1. 100% Updated with latest fully solved papers of Sept. 2023 2. Extensive Practice with 2200+ No. of Questions in Each Subject 3. Crisp Revision with Smart Mind Maps 4. Valuable Exam Insights with Expert Tips to crack CTET in first attempt 5. Concept Clarity with 15 solved papers (2013 to 2023) with Detailed Explanations 6. 100% Exam Readiness with 5 Years Chapter-wise Trend Analysis (2019-2023)

Praise for How Learning Works "How Learning Works is the perfect title for this excellent book. Drawing upon new research in psychology, education, and cognitive science, the authors have demystified a complex topic into clear explanations of seven powerful learning principles. Full of great ideas and practical suggestions, all based on solid research evidence, this book is essential reading for instructors at all levels who wish to improve their students' learning." —Barbara Gross Davis, assistant vice chancellor for educational development, University of California, Berkeley, and author, Tools for Teaching "This book is a must-read for every instructor, new or experienced. Although I have been teaching for almost thirty years, as I read this book I found myself resonating with many of its ideas, and I discovered new ways of thinking about teaching." —Eugenia T. Paulus, professor of chemistry, North Hennepin Community College, and 2008 U.S. Community Colleges Professor of the Year from The Carnegie Foundation for the Advancement of Teaching and the Council for Advancement and Support of Education "Thank you Carnegie Mellon for making accessible what has previously been inaccessible to those of us who are not learning scientists. Your focus on the essence of learning combined with concrete examples of the daily challenges of teaching and clear tactical strategies for faculty to consider is a welcome work. I will recommend this book to all my colleagues." —Catherine M. Casserly, senior partner, The Carnegie Foundation for the Advancement of Teaching "As you read about each of the seven basic learning principles in this book, you will find advice that is grounded in learning theory, based on research evidence, relevant to college teaching, and easy to understand. The authors have extensive knowledge and experience in applying the science of learning to college teaching, and they graciously share it with you in this organized and readable book." —From the Foreword by Richard E. Mayer, professor of psychology, University of California, Santa Barbara; coauthor, e-Learning and the Science of Instruction; and author, Multimedia Learning