Machine Learning for Econometrics and Related Topics PDF Download
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Author: Vladik Kreinovich
Publisher: Springer Nature
ISBN: 3031436016
Category :
Languages : en
Pages : 491
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Book Description
Author: Vladik Kreinovich
Publisher: Springer Nature
ISBN: 3031436016
Category :
Languages : en
Pages : 491
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Book Description
Author: Kevin Ferguson
Publisher: Simon and Schuster
ISBN: 1638354014
Category : Computers
Languages : en
Pages : 611
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Book Description
Summary Deep Learning and the Game of Go teaches you how to apply the power of deep learning to complex reasoning tasks by building a Go-playing AI. After exposing you to the foundations of machine and deep learning, you'll use Python to build a bot and then teach it the rules of the game. Foreword by Thore Graepel, DeepMind Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology The ancient strategy game of Go is an incredible case study for AI. In 2016, a deep learning-based system shocked the Go world by defeating a world champion. Shortly after that, the upgraded AlphaGo Zero crushed the original bot by using deep reinforcement learning to master the game. Now, you can learn those same deep learning techniques by building your own Go bot! About the Book Deep Learning and the Game of Go introduces deep learning by teaching you to build a Go-winning bot. As you progress, you'll apply increasingly complex training techniques and strategies using the Python deep learning library Keras. You'll enjoy watching your bot master the game of Go, and along the way, you'll discover how to apply your new deep learning skills to a wide range of other scenarios! What's inside Build and teach a self-improving game AI Enhance classical game AI systems with deep learning Implement neural networks for deep learning About the Reader All you need are basic Python skills and high school-level math. No deep learning experience required. About the Author Max Pumperla and Kevin Ferguson are experienced deep learning specialists skilled in distributed systems and data science. Together, Max and Kevin built the open source bot BetaGo. Table of Contents PART 1 - FOUNDATIONS Toward deep learning: a machine-learning introduction Go as a machine-learning problem Implementing your first Go bot PART 2 - MACHINE LEARNING AND GAME AI Playing games with tree search Getting started with neural networks Designing a neural network for Go data Learning from data: a deep-learning bot Deploying bots in the wild Learning by practice: reinforcement learning Reinforcement learning with policy gradients Reinforcement learning with value methods Reinforcement learning with actor-critic methods PART 3 - GREATER THAN THE SUM OF ITS PARTS AlphaGo: Bringing it all together AlphaGo Zero: Integrating tree search with reinforcement learning
Author: Ebru Çağlayan Akay
Publisher: Peter Lang Gmbh, Internationaler Verlag Der Wissenschaften
ISBN: 9783631795682
Category : Econometrics
Languages : en
Pages : 0
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Book Description
The book aims to bring together studies using different data types (panel data, cross-sectional data and time series data) and different methods (e.g., panel regression, nonlinear time series, chaos approach, among others) and to create a source for those interested in these topics and methods by addressing some selected applied econometrics topics.
Author: Ajay Agrawal
Publisher: University of Chicago Press
ISBN: 0226833127
Category : Business & Economics
Languages : en
Pages : 172
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Book Description
A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.
Author: Nguyen Ngoc Thach
Publisher: Springer Nature
ISBN: 3030488535
Category : Computers
Languages : en
Pages : 622
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Book Description
This book offers an overview of state-of-the-art econometric techniques, with a special emphasis on financial econometrics. There is a major need for such techniques, since the traditional way of designing mathematical models – based on researchers’ insights – can no longer keep pace with the ever-increasing data flow. To catch up, many application areas have begun relying on data science, i.e., on techniques for extracting models from data, such as data mining, machine learning, and innovative statistics. In terms of capitalizing on data science, many application areas are way ahead of economics. To close this gap, the book provides examples of how data science techniques can be used in economics. Corresponding techniques range from almost traditional statistics to promising novel ideas such as quantum econometrics. Given its scope, the book will appeal to students and researchers interested in state-of-the-art developments, and to practitioners interested in using data science techniques.
Author: Felix Chan
Publisher: Springer Nature
ISBN: 3031151496
Category : Business & Economics
Languages : en
Pages : 385
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Book Description
This book helps and promotes the use of machine learning tools and techniques in econometrics and explains how machine learning can enhance and expand the econometrics toolbox in theory and in practice. Throughout the volume, the authors raise and answer six questions: 1) What are the similarities between existing econometric and machine learning techniques? 2) To what extent can machine learning techniques assist econometric investigation? Specifically, how robust or stable is the prediction from machine learning algorithms given the ever-changing nature of human behavior? 3) Can machine learning techniques assist in testing statistical hypotheses and identifying causal relationships in ‘big data? 4) How can existing econometric techniques be extended by incorporating machine learning concepts? 5) How can new econometric tools and approaches be elaborated on based on machine learning techniques? 6) Is it possible to develop machine learning techniques further and make them even more readily applicable in econometrics? As the data structures in economic and financial data become more complex and models become more sophisticated, the book takes a multidisciplinary approach in developing both disciplines of machine learning and econometrics in conjunction, rather than in isolation. This volume is a must-read for scholars, researchers, students, policy-makers, and practitioners, who are using econometrics in theory or in practice.
Author: Tshepo Chris Nokeri
Publisher:
ISBN: 9781484283707
Category :
Languages : en
Pages : 0
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Book Description
Get up to speed on the application of machine learning approaches in macroeconomic research. This book brings together economics and data science. Author Tshepo Chris Nokeri begins by introducing you to covariance analysis, correlation analysis, cross-validation, hyperparameter optimization, regression analysis, and residual analysis. In addition, he presents an approach to contend with multi-collinearity. He then debunks a time series model recognized as the additive model. He reveals a technique for binarizing an economic feature to perform classification analysis using logistic regression. He brings in the Hidden Markov Model, used to discover hidden patterns and growth in the world economy. The author demonstrates unsupervised machine learning techniques such as principal component analysis and cluster analysis. Key deep learning concepts and ways of structuring artificial neural networks are explored along with training them and assessing their performance. The Monte Carlo simulation technique is applied to stimulate the purchasing power of money in an economy. Lastly, the Structural Equation Model (SEM) is considered to integrate correlation analysis, factor analysis, multivariate analysis, causal analysis, and path analysis. After reading this book, you should be able to recognize the connection between econometrics and data science. You will know how to apply a machine learning approach to modeling complex economic problems and others beyond this book. You will know how to circumvent and enhance model performance, together with the practical implications of a machine learning approach in econometrics, and you will be able to deal with pressing economic problems. What You Will Learn Examine complex, multivariate, linear-causal structures through the path and structural analysis technique, including non-linearity and hidden states Be familiar with practical applications of machine learning and deep learning in econometrics Understand theoretical framework and hypothesis development, and techniques for selecting appropriate models Develop, test, validate, and improve key supervised (i.e., regression and classification) and unsupervised (i.e., dimension reduction and cluster analysis) machine learning models, alongside neural networks, Markov, and SEM models Represent and interpret data and models .
Author: Atin Basuchoudhary
Publisher: Springer
ISBN: 3319690140
Category : Business & Economics
Languages : en
Pages : 97
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Book Description
This book develops a machine-learning framework for predicting economic growth. It can also be considered as a primer for using machine learning (also known as data mining or data analytics) to answer economic questions. While machine learning itself is not a new idea, advances in computing technology combined with a dawning realization of its applicability to economic questions makes it a new tool for economists.
Author: Gareth James
Publisher: Springer Nature
ISBN: 3031387473
Category : Mathematics
Languages : en
Pages : 617
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Book Description
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.
Author: Nguyen Ngoc Thach
Publisher: Springer Nature
ISBN: 303077094X
Category : Technology & Engineering
Languages : en
Pages : 691
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Book Description
This book provides the ultimate goal of economic studies to predict how the economy develops—and what will happen if we implement different policies. To be able to do that, we need to have a good understanding of what causes what in economics. Prediction and causality in economics are the main topics of this book's chapters; they use both more traditional and more innovative techniques—including quantum ideas -- to make predictions about the world economy (international trade, exchange rates), about a country's economy (gross domestic product, stock index, inflation rate), and about individual enterprises, banks, and micro-finance institutions: their future performance (including the risk of bankruptcy), their stock prices, and their liquidity. Several papers study how COVID-19 has influenced the world economy. This book helps practitioners and researchers to learn more about prediction and causality in economics -- and to further develop this important research direction.
