'Introduction to type introduces you to key learning strategies and learning style information. Designed for adult learners who want to take control of their learning, it provides a comprehensive guide for enhancing learning effectiveness. Whether you are returning to school , learning on the job, or developing skills and knowledge related to your personal interests, the booklet will help you to identify your learning style and develop and apply strategies that suit your learning preferences'-- taken from Introduction.
Broaden your understanding of personality type with the Introduction to Type series from CCP. These popular guides help you integrate type theory concepts into both your personal and professional lives.Understanding workplace preferences, managing stress, reducing conflict, searching for suitable careers, and improving team effectiveness are just a few of the many type-related applications you can explore using the MBTI booklets.
Broaden your understanding of personality type with the Introduction to Type series from CCP. These popular guides help you integrate type theory concepts into both your personal and professional lives.Understanding workplace preferences, managing stress, reducing conflict, searching for suitable careers, and improving team effectiveness are just a few of the many type-related applications you can explore using the MBTI booklets.
There are many reasons to be curious about the way people learn, and the past several decades have seen an explosion of research that has important implications for individual learning, schooling, workforce training, and policy. In 2000, How People Learn: Brain, Mind, Experience, and School: Expanded Edition was published and its influence has been wide and deep. The report summarized insights on the nature of learning in school-aged children; described principles for the design of effective learning environments; and provided examples of how that could be implemented in the classroom. Since then, researchers have continued to investigate the nature of learning and have generated new findings related to the neurological processes involved in learning, individual and cultural variability related to learning, and educational technologies. In addition to expanding scientific understanding of the mechanisms of learning and how the brain adapts throughout the lifespan, there have been important discoveries about influences on learning, particularly sociocultural factors and the structure of learning environments. How People Learn II: Learners, Contexts, and Cultures provides a much-needed update incorporating insights gained from this research over the past decade. The book expands on the foundation laid out in the 2000 report and takes an in-depth look at the constellation of influences that affect individual learning. How People Learn II will become an indispensable resource to understand learning throughout the lifespan for educators of students and adults.
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.
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
A survival guide companion for students beginning their first online or hybrid class Introduction to Online Learning introduces first-time distance learners to the realities of Web-based education and serves as the most comprehensive, practical guide to achieving success when facing online-specific barriers as well as common academic hurdles. Written by an experienced instructor, this invaluable aid shows students how to overcome challenges related to e-mail communication, technological catastrophes, staying organized on a daily basis, and more. Students learn to take advantage of the unique resources available for those enrolled in internet-based programs and to make the most of their Web-based educational experience by tailoring it to their personal strengths, needs, and learning styles. Key Features The author provides clear explanations of how to tailor research, writing, and citing sources to the online classroom, as well as when citations are necessary Concrete, original examples link the text to students′ personal experiences; illustrations vividly bring material to life Screenshots and excerpts from online syllabi help students navigate their first course requirements Examples of appropriate discussion board interaction aid students in progressing in their course with confidence Self-assessments guide students in determining individual learning styles and levels of preparedness The open-access student study site includes first-person testimonials and advice from online students and instructors, links to relevant Web sites and resources, and self-quizzes. Intended Audience This groundbreaking supplement is a must-have for any student enrolled in an online course or degree program, or for students enrolled in hybrid courses including a mix of online and classroom learning.
