The Frontiers Of Machine Learning

Author : The Royal Society,National Academy of Sciences
Publisher : National Academies Press
Page : 33 pages
File Size : 44,5 Mb
Release : 2018-02-09
Category : Technology & Engineering
ISBN : 9780309471978

Get Book

The Frontiers of Machine Learning by The Royal Society,National Academy of Sciences Pdf

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 : Bhavya Kailkhura,Xue Lin,Pin-Yu Chen,Bo Li
Publisher : Frontiers Media SA
Page : 101 pages
File Size : 51,6 Mb
Release : 2021-10-29
Category : Science
ISBN : 9782889714148

Get Book

Safe and Trustworthy Machine Learning by Bhavya Kailkhura,Xue Lin,Pin-Yu Chen,Bo Li Pdf

Author : Mohit Sewak
Publisher : Springer
Page : 203 pages
File Size : 49,9 Mb
Release : 2019-06-27
Category : Computers
ISBN : 9789811382857

Get Book

Deep Reinforcement Learning by Mohit Sewak Pdf

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 : Lei Deng,Kaushik Roy,Huajin Tang
Publisher : Frontiers Media SA
Page : 200 pages
File Size : 49,6 Mb
Release : 2021-05-05
Category : Science
ISBN : 9782889667420

Get Book

Understanding and Bridging the Gap between Neuromorphic Computing and Machine Learning by Lei Deng,Kaushik Roy,Huajin Tang Pdf

Author : Chaopeng Shen,Eric Laloy,Xingyuan Chen
Publisher : Frontiers Media SA
Page : 163 pages
File Size : 53,8 Mb
Release : 2021-07-08
Category : Science
ISBN : 9782889669820

Get Book

Broadening the Use of Machine Learning in Hydrology by Chaopeng Shen,Eric Laloy,Xingyuan Chen Pdf

Author : Gennady Verkhivker,Vojtech Spiwok,Francesco L. Gervasio
Publisher : Frontiers Media SA
Page : 129 pages
File Size : 51,8 Mb
Release : 2019-10-21
Category : Electronic
ISBN : 9782889631360

Get Book

Machine Learning in Biomolecular Simulations by Gennady Verkhivker,Vojtech Spiwok,Francesco L. Gervasio Pdf

Machine learning methods such as neural networks, non-linear dimensionality reduction techniques, random forests and others meet in this research topic with biomolecular simulations. The authors of eight articles applied these methods to analyze simulation results, accelerate simulations or to make molecular mechanics force fields more accurate.

Author : Kun Qian,Liang Zhang,Kezhi Li,Juan Liu
Publisher : Frontiers Media SA
Page : 174 pages
File Size : 47,7 Mb
Release : 2021-11-26
Category : Medical
ISBN : 9782889717088

Get Book

Machine Learning for Non/Less-Invasive Methods in Health Informatics by Kun Qian,Liang Zhang,Kezhi Li,Juan Liu Pdf

Author : Siddhartha Bhattacharyya,Vaclav Snasel,Aboul Ella Hassanien,Satadal Saha,B. K. Tripathy
Publisher : Walter de Gruyter GmbH & Co KG
Page : 161 pages
File Size : 46,8 Mb
Release : 2020-06-22
Category : Computers
ISBN : 9783110670905

Get Book

Deep Learning by Siddhartha Bhattacharyya,Vaclav Snasel,Aboul Ella Hassanien,Satadal Saha,B. K. Tripathy Pdf

This book focuses on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it provides an insight of deep neural networks in action with illustrative coding examples. Deep learning is a new area of machine learning research which has been introduced with the objective of moving ML closer to one of its original goals, i.e. artificial intelligence. Deep learning was developed as an ML approach to deal with complex input-output mappings. While traditional methods successfully solve problems where final value is a simple function of input data, deep learning techniques are able to capture composite relations between non-immediately related fields, for example between air pressure recordings and English words, millions of pixels and textual description, brand-related news and future stock prices and almost all real world problems. Deep learning is a class of nature inspired machine learning algorithms that uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The learning may be supervised (e.g. classification) and/or unsupervised (e.g. pattern analysis) manners. These algorithms learn multiple levels of representations that correspond to different levels of abstraction by resorting to some form of gradient descent for training via backpropagation. Layers that have been used in deep learning include hidden layers of an artificial neural network and sets of propositional formulas. They may also include latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep boltzmann machines. Deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision, automatic speech recognition (ASR) and human action recognition.

Author : Matthias Dehmer,Frank Emmert-Streib
Publisher : CRC Press
Page : 391 pages
File Size : 43,7 Mb
Release : 2017-10-16
Category : Computers
ISBN : 9781351643245

Get Book

Frontiers in Data Science by Matthias Dehmer,Frank Emmert-Streib Pdf

Frontiers in Data Science deals with philosophical and practical results in Data Science. A broad definition of Data Science describes the process of analyzing data to transform data into insights. This also involves asking philosophical, legal and social questions in the context of data generation and analysis. In fact, Big Data also belongs to this universe as it comprises data gathering, data fusion and analysis when it comes to manage big data sets. A major goal of this book is to understand data science as a new scientific discipline rather than the practical aspects of data analysis alone.

Author : Thomas Berger,Enrico Camporeale,Bala Poduval,Veronique A. Delouille,Sophie A. Murray
Publisher : Frontiers Media SA
Page : 240 pages
File Size : 49,8 Mb
Release : 2021-11-24
Category : Science
ISBN : 9782889716715

Get Book

Machine Learning in Heliophysics by Thomas Berger,Enrico Camporeale,Bala Poduval,Veronique A. Delouille,Sophie A. Murray Pdf

Author : Zhi-Hua Zhou
Publisher : Springer Nature
Page : 460 pages
File Size : 42,6 Mb
Release : 2021-08-20
Category : Computers
ISBN : 9789811519673

Get Book

Machine Learning by Zhi-Hua Zhou Pdf

Machine Learning, a vital and core area of artificial intelligence (AI), is propelling the AI field ever further and making it one of the most compelling areas of computer science research. This textbook offers a comprehensive and unbiased introduction to almost all aspects of machine learning, from the fundamentals to advanced topics. It consists of 16 chapters divided into three parts: Part 1 (Chapters 1-3) introduces the fundamentals of machine learning, including terminology, basic principles, evaluation, and linear models; Part 2 (Chapters 4-10) presents classic and commonly used machine learning methods, such as decision trees, neural networks, support vector machines, Bayesian classifiers, ensemble methods, clustering, dimension reduction and metric learning; Part 3 (Chapters 11-16) introduces some advanced topics, covering feature selection and sparse learning, computational learning theory, semi-supervised learning, probabilistic graphical models, rule learning, and reinforcement learning. Each chapter includes exercises and further reading, so that readers can explore areas of interest. The book can be used as an undergraduate or postgraduate textbook for computer science, computer engineering, electrical engineering, data science, and related majors. It is also a useful reference resource for researchers and practitioners of machine learning.

Author : Alessandro Palmas,Emanuele Ghelfi,Dr. Alexandra Galina Petre,Mayur Kulkarni,Anand N.S.,Quan Nguyen,Aritra Sen,Anthony So,Saikat Basak
Publisher : Packt Publishing Ltd
Page : 821 pages
File Size : 40,9 Mb
Release : 2020-08-18
Category : Computers
ISBN : 9781800209961

Get Book

The The Reinforcement Learning Workshop by Alessandro Palmas,Emanuele Ghelfi,Dr. Alexandra Galina Petre,Mayur Kulkarni,Anand N.S.,Quan Nguyen,Aritra Sen,Anthony So,Saikat Basak Pdf

Start with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning, deep recurrent Q-networks, and policy-based methods with this practical guide Key FeaturesUse TensorFlow to write reinforcement learning agents for performing challenging tasksLearn how to solve finite Markov decision problemsTrain models to understand popular video games like BreakoutBook Description Various intelligent applications such as video games, inventory management software, warehouse robots, and translation tools use reinforcement learning (RL) to make decisions and perform actions that maximize the probability of the desired outcome. This book will help you to get to grips with the techniques and the algorithms for implementing RL in your machine learning models. Starting with an introduction to RL, you’ll be guided through different RL environments and frameworks. You’ll learn how to implement your own custom environments and use OpenAI baselines to run RL algorithms. Once you’ve explored classic RL techniques such as Dynamic Programming, Monte Carlo, and TD Learning, you’ll understand when to apply the different deep learning methods in RL and advance to deep Q-learning. The book will even help you understand the different stages of machine-based problem-solving by using DARQN on a popular video game Breakout. Finally, you’ll find out when to use a policy-based method to tackle an RL problem. By the end of The Reinforcement Learning Workshop, you’ll be equipped with the knowledge and skills needed to solve challenging problems using reinforcement learning. What you will learnUse OpenAI Gym as a framework to implement RL environmentsFind out how to define and implement reward functionExplore Markov chain, Markov decision process, and the Bellman equationDistinguish between Dynamic Programming, Monte Carlo, and Temporal Difference LearningUnderstand the multi-armed bandit problem and explore various strategies to solve itBuild a deep Q model network for playing the video game BreakoutWho this book is for If you are a data scientist, machine learning enthusiast, or a Python developer who wants to learn basic to advanced deep reinforcement learning algorithms, this workshop is for you. A basic understanding of the Python language is necessary.

Author : A.J. Tallón-Ballesteros,C.-H. Chen
Publisher : IOS Press
Page : 482 pages
File Size : 42,5 Mb
Release : 2020-12-15
Category : Computers
ISBN : 9781643681375

Get Book

Machine Learning and Artificial Intelligence by A.J. Tallón-Ballesteros,C.-H. Chen Pdf

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 : Matthew F. Dixon,Igor Halperin,Paul Bilokon
Publisher : Springer Nature
Page : 565 pages
File Size : 54,6 Mb
Release : 2020-07-01
Category : Business & Economics
ISBN : 9783030410681

Get Book

Machine Learning in Finance by Matthew F. Dixon,Igor Halperin,Paul Bilokon Pdf

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 : Lingfei Wu,Peng Cui,Jian Pei,Liang Zhao
Publisher : Springer Nature
Page : 701 pages
File Size : 49,6 Mb
Release : 2022-01-03
Category : Computers
ISBN : 9789811660542

Get Book

Graph Neural Networks: Foundations, Frontiers, and Applications by Lingfei Wu,Peng Cui,Jian Pei,Liang Zhao Pdf

Deep Learning models are at the core of artificial intelligence research today. It is well known that deep learning techniques are disruptive for Euclidean data, such as images or sequence data, and not immediately applicable to graph-structured data such as text. This gap has driven a wave of research for deep learning on graphs, including graph representation learning, graph generation, and graph classification. The new neural network architectures on graph-structured data (graph neural networks, GNNs in short) have performed remarkably on these tasks, demonstrated by applications in social networks, bioinformatics, and medical informatics. Despite these successes, GNNs still face many challenges ranging from the foundational methodologies to the theoretical understandings of the power of the graph representation learning. This book provides a comprehensive introduction of GNNs. It first discusses the goals of graph representation learning and then reviews the history, current developments, and future directions of GNNs. The second part presents and reviews fundamental methods and theories concerning GNNs while the third part describes various frontiers that are built on the GNNs. The book concludes with an overview of recent developments in a number of applications using GNNs. This book is suitable for a wide audience including undergraduate and graduate students, postdoctoral researchers, professors and lecturers, as well as industrial and government practitioners who are new to this area or who already have some basic background but want to learn more about advanced and promising techniques and applications.