Learning from Data
Author | : Yaser S. Abu-Mostafa |
Publisher | : |
Total Pages | : 201 |
Release | : 2012-01-01 |
ISBN-10 | : 1600490069 |
ISBN-13 | : 9781600490064 |
Rating | : 4/5 (69 Downloads) |
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Author | : Yaser S. Abu-Mostafa |
Publisher | : |
Total Pages | : 201 |
Release | : 2012-01-01 |
ISBN-10 | : 1600490069 |
ISBN-13 | : 9781600490064 |
Rating | : 4/5 (69 Downloads) |
Author | : Vladimir Cherkassky |
Publisher | : John Wiley & Sons |
Total Pages | : 560 |
Release | : 2007-09-10 |
ISBN-10 | : 0470140518 |
ISBN-13 | : 9780470140512 |
Rating | : 4/5 (18 Downloads) |
An interdisciplinary framework for learning methodologies—covering statistics, neural networks, and fuzzy logic, this book provides a unified treatment of the principles and methods for learning dependencies from data. It establishes a general conceptual framework in which various learning methods from statistics, neural networks, and fuzzy logic can be applied—showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science. Complete with over one hundred illustrations, case studies, and examples making this an invaluable text.
Author | : David Spiegelhalter |
Publisher | : Basic Books |
Total Pages | : 359 |
Release | : 2019-09-03 |
ISBN-10 | : 9781541618527 |
ISBN-13 | : 1541618521 |
Rating | : 4/5 (27 Downloads) |
In this "important and comprehensive" guide to statistical thinking (New Yorker), discover how data literacy is changing the world and gives you a better understanding of life’s biggest problems. Statistics are everywhere, as integral to science as they are to business, and in the popular media hundreds of times a day. In this age of big data, a basic grasp of statistical literacy is more important than ever if we want to separate the fact from the fiction, the ostentatious embellishments from the raw evidence -- and even more so if we hope to participate in the future, rather than being simple bystanders. In The Art of Statistics, world-renowned statistician David Spiegelhalter shows readers how to derive knowledge from raw data by focusing on the concepts and connections behind the math. Drawing on real world examples to introduce complex issues, he shows us how statistics can help us determine the luckiest passenger on the Titanic, whether a notorious serial killer could have been caught earlier, and if screening for ovarian cancer is beneficial. The Art of Statistics not only shows us how mathematicians have used statistical science to solve these problems -- it teaches us how we too can think like statisticians. We learn how to clarify our questions, assumptions, and expectations when approaching a problem, and -- perhaps even more importantly -- we learn how to responsibly interpret the answers we receive. Combining the incomparable insight of an expert with the playful enthusiasm of an aficionado, The Art of Statistics is the definitive guide to stats that every modern person needs.
Author | : Gilbert Strang |
Publisher | : Wellesley-Cambridge Press |
Total Pages | : 0 |
Release | : 2019-01-31 |
ISBN-10 | : 0692196382 |
ISBN-13 | : 9780692196380 |
Rating | : 4/5 (82 Downloads) |
Linear algebra and the foundations of deep learning, together at last! From Professor Gilbert Strang, acclaimed author of Introduction to Linear Algebra, comes Linear Algebra and Learning from Data, the first textbook that teaches linear algebra together with deep learning and neural nets. This readable yet rigorous textbook contains a complete course in the linear algebra and related mathematics that students need to know to get to grips with learning from data. Included are: the four fundamental subspaces, singular value decompositions, special matrices, large matrix computation techniques, compressed sensing, probability and statistics, optimization, the architecture of neural nets, stochastic gradient descent and backpropagation.
Author | : Craig Friedman |
Publisher | : CRC Press |
Total Pages | : 418 |
Release | : 2016-04-19 |
ISBN-10 | : 9781420011289 |
ISBN-13 | : 1420011286 |
Rating | : 4/5 (89 Downloads) |
Utility-Based Learning from Data provides a pedagogical, self-contained discussion of probability estimation methods via a coherent approach from the viewpoint of a decision maker who acts in an uncertain environment. This approach is motivated by the idea that probabilistic models are usually not learned for their own sake; rather, they are used t
Author | : Ellen B. Mandinach |
Publisher | : Corwin Press |
Total Pages | : 281 |
Release | : 2012-04-10 |
ISBN-10 | : 9781412982047 |
ISBN-13 | : 1412982049 |
Rating | : 4/5 (47 Downloads) |
"Gathering data and using it to inform instruction is a requirement for many schools, yet educators are not necessarily formally trained in how to do it. This book helps bridge the gap between classroom practice and the principles of educational psychology. Teachers will find cutting-edge advances in research and theory on human learning and teaching in an easily understood and transferable format. The text's integrated model shows teachers, school leaders, and district administrators how to establish a data culture and transform quantitative and qualitative data into actionable knowledge based on: assessment; statistics; instructional and differentiated psychology; classroom management."--Publisher's description.
Author | : Philippe J. S. De Brouwer |
Publisher | : John Wiley & Sons |
Total Pages | : 928 |
Release | : 2020-10-27 |
ISBN-10 | : 9781119632726 |
ISBN-13 | : 1119632722 |
Rating | : 4/5 (26 Downloads) |
Introduces professionals and scientists to statistics and machine learning using the programming language R Written by and for practitioners, this book provides an overall introduction to R, focusing on tools and methods commonly used in data science, and placing emphasis on practice and business use. It covers a wide range of topics in a single volume, including big data, databases, statistical machine learning, data wrangling, data visualization, and the reporting of results. The topics covered are all important for someone with a science/math background that is looking to quickly learn several practical technologies to enter or transition to the growing field of data science. The Big R-Book for Professionals: From Data Science to Learning Machines and Reporting with R includes nine parts, starting with an introduction to the subject and followed by an overview of R and elements of statistics. The third part revolves around data, while the fourth focuses on data wrangling. Part 5 teaches readers about exploring data. In Part 6 we learn to build models, Part 7 introduces the reader to the reality in companies, Part 8 covers reports and interactive applications and finally Part 9 introduces the reader to big data and performance computing. It also includes some helpful appendices. Provides a practical guide for non-experts with a focus on business users Contains a unique combination of topics including an introduction to R, machine learning, mathematical models, data wrangling, and reporting Uses a practical tone and integrates multiple topics in a coherent framework Demystifies the hype around machine learning and AI by enabling readers to understand the provided models and program them in R Shows readers how to visualize results in static and interactive reports Supplementary materials includes PDF slides based on the book’s content, as well as all the extracted R-code and is available to everyone on a Wiley Book Companion Site The Big R-Book is an excellent guide for science technology, engineering, or mathematics students who wish to make a successful transition from the academic world to the professional. It will also appeal to all young data scientists, quantitative analysts, and analytics professionals, as well as those who make mathematical models.
Author | : Philip D. Laird |
Publisher | : Springer Science & Business Media |
Total Pages | : 223 |
Release | : 2012-12-06 |
ISBN-10 | : 9781461316855 |
ISBN-13 | : 1461316855 |
Rating | : 4/5 (55 Downloads) |
This monograph is a contribution to the study of the identification problem: the problem of identifying an item from a known class us ing positive and negative examples. This problem is considered to be an important component of the process of inductive learning, and as such has been studied extensively. In the overview we shall explain the objectives of this work and its place in the overall fabric of learning research. Context. Learning occurs in many forms; the only form we are treat ing here is inductive learning, roughly characterized as the process of forming general concepts from specific examples. Computer Science has found three basic approaches to this problem: • Select a specific learning task, possibly part of a larger task, and construct a computer program to solve that task . • Study cognitive models of learning in humans and extrapolate from them general principles to explain learning behavior. Then construct machine programs to test and illustrate these models. xi Xll PREFACE • Formulate a mathematical theory to capture key features of the induction process. This work belongs to the third category. The various studies of learning utilize training examples (data) in different ways. The three principal ones are: • Similarity-based (or empirical) learning, in which a collection of examples is used to select an explanation from a class of possible rules.
Author | : Steven L. Brunton |
Publisher | : Cambridge University Press |
Total Pages | : 615 |
Release | : 2022-05-05 |
ISBN-10 | : 9781009098489 |
ISBN-13 | : 1009098489 |
Rating | : 4/5 (89 Downloads) |
A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
Author | : Luca Oneto |
Publisher | : Springer |
Total Pages | : 221 |
Release | : 2021-04-04 |
ISBN-10 | : 3030438856 |
ISBN-13 | : 9783030438852 |
Rating | : 4/5 (56 Downloads) |
This book offers a timely snapshot and extensive practical and theoretical insights into the topic of learning from data. Based on the tutorials presented at the INNS Big Data and Deep Learning Conference, INNSBDDL2019, held on April 16-18, 2019, in Sestri Levante, Italy, the respective chapters cover advanced neural networks, deep architectures, and supervised and reinforcement machine learning models. They describe important theoretical concepts, presenting in detail all the necessary mathematical formalizations, and offer essential guidance on their use in current big data research.