Explains effective and efficient study methods for students to improve exam and academic performance, describing the author's "Concise Learning Method" (CLM), and featuring thirteen two-page visual maps of essential skills

The study of learning and memory is a central topic in neuroscience and psychology. Many of the basic research findings are directly applicable in the treatment of diseases and aging phenomena, and have found their way into educational theory and praxis. Concise Learning and Memory represents the best 30 chapters from Learning and Memory: A comprehensive reference (Academic Press March 2008), the most comprehensive source of information about learning and memory ever assembled, selected by one of the most respective scientists in the field, John H. Byrne. This concise version provides a truly authoritative collection of overview articles representing fundamental reviews of our knowledge of this central cognitive function of animal brains. It will be an affordable and accessible reference for scientists and students in all areas of neuroscience and psychology. There is no other single-volume reference with such authority and comprehensive coverage and depth currently available. - Represents an authoritative selection of the fundamental chapters from the most comprehensive source of information about learning and memory ever assembled, Learning and Memory - A comprehensive reference (Academic Press Mar 2008) - Representing outstanding scholarship, each chapter is written by a leader in the field and an expert in the topic area - All topics represent the most up to date research - Full color throughout, heavily illustrated - Priced to provide an affordable reference to individuals and workgroups

The emphasis of the book is on the question of Why – only if why an algorithm is successful is understood, can it be properly applied, and the results trusted. Algorithms are often taught side by side without showing the similarities and differences between them. This book addresses the commonalities, and aims to give a thorough and in-depth treatment and develop intuition, while remaining concise. This useful reference should be an essential on the bookshelves of anyone employing machine learning techniques. The author's webpage for the book can be accessed here.

AN INTRODUCTION TO MACHINE LEARNING THAT INCLUDES THE FUNDAMENTAL TECHNIQUES, METHODS, AND APPLICATIONS PROSE Award Finalist 2019 Association of American Publishers Award for Professional and Scholarly Excellence Machine Learning: a Concise Introduction offers a comprehensive introduction to the core concepts, approaches, and applications of machine learning. The author—an expert in the field—presents fundamental ideas, terminology, and techniques for solving applied problems in classification, regression, clustering, density estimation, and dimension reduction. The design principles behind the techniques are emphasized, including the bias-variance trade-off and its influence on the design of ensemble methods. Understanding these principles leads to more flexible and successful applications. Machine Learning: a Concise Introduction also includes methods for optimization, risk estimation, and model selection— essential elements of most applied projects. This important resource: Illustrates many classification methods with a single, running example, highlighting similarities and differences between methods Presents R source code which shows how to apply and interpret many of the techniques covered Includes many thoughtful exercises as an integral part of the text, with an appendix of selected solutions Contains useful information for effectively communicating with clients A volume in the popular Wiley Series in Probability and Statistics, Machine Learning: a Concise Introduction offers the practical information needed for an understanding of the methods and application of machine learning. STEVEN W. KNOX holds a Ph.D. in Mathematics from the University of Illinois and an M.S. in Statistics from Carnegie Mellon University. He has over twenty years’ experience in using Machine Learning, Statistics, and Mathematics to solve real-world problems. He currently serves as Technical Director of Mathematics Research and Senior Advocate for Data Science at the National Security Agency.

Medical Terminology Quick & Concise: A Programmed Learning Approach is a unique combination of core medical terminology and a programmed self-study approach that allows you to easily master and apply the building blocks of medical terminology.

This concise guidebook on desirable difficulties is designed to be a resource for academics who are interested in engaging students according to the findings of peer-reviewed literature and best practices but do not have the time to immerse themselves in the scholarship of teaching and learning.Intentionally brief, the book is intended to: summarize recent research on five aspects of desirable difficulties; provide applications to the college classroom based on this research; include special sections about teaching strategies that are based on best practices; and offer annotated bibliographies and important citations for faculty who want to pursue additional study. The book will provide a foundation for instructors to teach using evidence-based strategies that will strengthen learning and retention in their classrooms.In addition to chapters on the desirable difficulties, the book also includes chapters on teaching first-year and at-risk students to embrace this approach, on negotiating student resistance, and on using this approach in teaching online.

This lucid, accessible introduction to supervised machine learning presents core concepts in a focused and logical way that is easy for beginners to follow. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning. Coverage includes widely used traditional methods such as SVMs, boosted trees, HMMs, and LDAs, plus popular deep learning methods such as convolution neural nets, attention, transformers, and GANs. Organized in a coherent presentation framework that emphasizes the big picture, the text introduces each method clearly and concisely “from scratch” based on the fundamentals. All methods and algorithms are described by a clean and consistent style, with a minimum of unnecessary detail. Numerous case studies and concrete examples demonstrate how the methods can be applied in a variety of contexts.

This concise guidebook is intended for faculty who are interested in engaging their students and developing deep and lasting learning, but do not have the time to immerse themselves in the scholarship of teaching and learning. Acknowledging the growing body of peer-reviewed literature on practices that can dramatically impact teaching, this intentionally brief book:* Summarizes recent research on six of the most compelling principles in learning and teaching* Describes their application to the college classroom* Presents teaching strategies that are based on pragmatic practices* Provides annotated bibliographies and important citations for faculty who want to explore these topics further This guidebook begins with an overview of how we learn, covering such topics such as the distinction between expert and novice learners, memory, prior learning, and metacognition. The body of the book is divided into three main sections each of which includes teaching principles, applications, and related strategies – most of which can be implemented without extensive preparation.The applications sections present examples of practice across a diverse range of disciplines including the sciences, humanities, arts, and pre-professional programs. This book provides a foundation for the reader explore these approaches and methods in his or her teaching.

Planning is the model-based approach to autonomous behavior where the agent behavior is derived automatically from a model of the actions, sensors, and goals. The main challenges in planning are computational as all models, whether featuring uncertainty and feedback or not, are intractable in the worst case when represented in compact form. In this book, we look at a variety of models used in AI planning, and at the methods that have been developed for solving them. The goal is to provide a modern and coherent view of planning that is precise, concise, and mostly self-contained, without being shallow. For this, we make no attempt at covering the whole variety of planning approaches, ideas, and applications, and focus on the essentials. The target audience of the book are students and researchers interested in autonomous behavior and planning from an AI, engineering, or cognitive science perspective. Table of Contents: Preface / Planning and Autonomous Behavior / Classical Planning: Full Information and Deterministic Actions / Classical Planning: Variations and Extensions / Beyond Classical Planning: Transformations / Planning with Sensing: Logical Models / MDP Planning: Stochastic Actions and Full Feedback / POMDP Planning: Stochastic Actions and Partial Feedback / Discussion / Bibliography / Author's Biography