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Author: The Royal Society Publisher: National Academies Press ISBN: 0309471974 Category : Technology & Engineering Languages : en Pages : 33
Book Description
The field of machine learning continues to advance at a rapid pace owing to increased computing power, better algorithms and tools, and greater availability of data. Machine learning is now being used in a range of applications, including transportation and developing automated vehicles, healthcare and understanding the genetic basis of disease, and criminal justice and predicting recidivism. As the technology advances, it promises additional applications that can contribute to individual and societal well-being. The Raymond and Beverly Sackler U.S.-U.K. Scientific Forum "The Frontiers
 of Machine Learning" took place on January 31 and February 1, 2017, at the Washington, D.C., headquarters of the National Academies of Sciences, Engineering, and Medicine. Participants included industry leaders, machine learning researchers, and experts in privacy and the law, and this report summarizes their high-level interdisciplinary discussions.
Author: The Royal Society Publisher: National Academies Press ISBN: 0309471974 Category : Technology & Engineering Languages : en Pages : 33
Book Description
The field of machine learning continues to advance at a rapid pace owing to increased computing power, better algorithms and tools, and greater availability of data. Machine learning is now being used in a range of applications, including transportation and developing automated vehicles, healthcare and understanding the genetic basis of disease, and criminal justice and predicting recidivism. As the technology advances, it promises additional applications that can contribute to individual and societal well-being. The Raymond and Beverly Sackler U.S.-U.K. Scientific Forum "The Frontiers
 of Machine Learning" took place on January 31 and February 1, 2017, at the Washington, D.C., headquarters of the National Academies of Sciences, Engineering, and Medicine. Participants included industry leaders, machine learning researchers, and experts in privacy and the law, and this report summarizes their high-level interdisciplinary discussions.
Author: Matthew F. Dixon Publisher: Springer Nature ISBN: 3030410684 Category : Business & Economics Languages : en Pages : 565
Book Description
This book introduces machine learning methods in finance. It presents a unified treatment of machine learning and various statistical and computational disciplines in quantitative finance, such as financial econometrics and discrete time stochastic control, with an emphasis on how theory and hypothesis tests inform the choice of algorithm for financial data modeling and decision making. With the trend towards increasing computational resources and larger datasets, machine learning has grown into an important skillset for the finance industry. This book is written for advanced graduate students and academics in financial econometrics, mathematical finance and applied statistics, in addition to quants and data scientists in the field of quantitative finance. Machine Learning in Finance: From Theory to Practice is divided into three parts, each part covering theory and applications. The first presents supervised learning for cross-sectional data from both a Bayesian and frequentist perspective. The more advanced material places a firm emphasis on neural networks, including deep learning, as well as Gaussian processes, with examples in investment management and derivative modeling. The second part presents supervised learning for time series data, arguably the most common data type used in finance with examples in trading, stochastic volatility and fixed income modeling. Finally, the third part presents reinforcement learning and its applications in trading, investment and wealth management. Python code examples are provided to support the readers' understanding of the methodologies and applications. The book also includes more than 80 mathematical and programming exercises, with worked solutions available to instructors. As a bridge to research in this emergent field, the final chapter presents the frontiers of machine learning in finance from a researcher's perspective, highlighting how many well-known concepts in statistical physics are likely to emerge as important methodologies for machine learning in finance.
Author: Mohit Sewak Publisher: Springer ISBN: 9811382859 Category : Computers Languages : en Pages : 203
Book Description
This book starts by presenting the basics of reinforcement learning using highly intuitive and easy-to-understand examples and applications, and then introduces the cutting-edge research advances that make reinforcement learning capable of out-performing most state-of-art systems, and even humans in a number of applications. The book not only equips readers with an understanding of multiple advanced and innovative algorithms, but also prepares them to implement systems such as those created by Google Deep Mind in actual code. This book is intended for readers who want to both understand and apply advanced concepts in a field that combines the best of two worlds – deep learning and reinforcement learning – to tap the potential of ‘advanced artificial intelligence’ for creating real-world applications and game-winning algorithms.
Author: A.J. Tallón-Ballesteros Publisher: IOS Press ISBN: 1643681370 Category : Computers Languages : en Pages : 482
Book Description
Machine learning and artificial intelligence are already widely applied to facilitate our daily lives, as well as scientific research, but with the world currently facing a global COVID-19 pandemic, their capacity to provide an important tool to support those searching for a way to combat the novel corona virus has never been more important. This book presents the proceedings of the International Conference on Machine Learning and Intelligent Systems (MLIS 2020), which was due to be held in Seoul, Korea, from 25-28 October 2020, but which was delivered as an online conference on the same dates due to COVID-19 restrictions. MLIS 2020 was the latest in a series of annual conferences that aim to provide a platform for exchanging knowledge about the most recent scientific and technological advances in the field of machine learning and intelligent systems. The annual conference also strengthens links within the scientific community in related research areas. The book contains 53 papers, selected from more than 160 submissions and presented at MLIS 2020. Selection was based on the results of review and scored on: originality, scientific/practical significance, compelling logical reasoning and language. Topics covered include: data mining, image processing, neural networks, human health, natural language processing, video processing, computational intelligence, expert systems, human-computer interaction, deep learning, and robotics. Offering a current overview of research and developments in machine learning and artificial intelligence, the book will be of interest to all those working in the field.
Author: Simon Rogers Publisher: CRC Press ISBN: 1498738540 Category : Computers Languages : en Pages : 428
Book Description
Introduces the main algorithms and ideas that underpin machine learning techniques and applications Keeps mathematical prerequisites to a minimum, providing mathematical explanations in comment boxes and highlighting important equations Covers modern machine learning research and techniques Includes three new chapters on Markov Chain Monte Carlo techniques, Classification and Regression with Gaussian Processes, and Dirichlet Process models Offers Python, R, and MATLAB code on accompanying website: http://www.dcs.gla.ac.uk/~srogers/firstcourseml/"
Author: Zhouchen Lin Publisher: Springer Nature ISBN: 9811529108 Category : Computers Languages : en Pages : 286
Book Description
This book on optimization includes forewords by Michael I. Jordan, Zongben Xu and Zhi-Quan Luo. Machine learning relies heavily on optimization to solve problems with its learning models, and first-order optimization algorithms are the mainstream approaches. The acceleration of first-order optimization algorithms is crucial for the efficiency of machine learning. Written by leading experts in the field, this book provides a comprehensive introduction to, and state-of-the-art review of accelerated first-order optimization algorithms for machine learning. It discusses a variety of methods, including deterministic and stochastic algorithms, where the algorithms can be synchronous or asynchronous, for unconstrained and constrained problems, which can be convex or non-convex. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference resource for users who are seeking faster optimization algorithms, as well as for graduate students and researchers wanting to grasp the frontiers of optimization in machine learning in a short time.
Author: The Royal Society
Author: The Royal Society
Author: Matthew F. Dixon
Author: Mohit Sewak
Author: Chaopeng Shen
Author: Bhavya Kailkhura
Author: Lei Deng
Author: Thomas Berger
Author: A.J. Tallón-Ballesteros
Author: Simon Rogers
Author: Zhouchen Lin