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Patterns, Predictions, and Actions: Foundations of Machine Learning

Moritz Hardt,Benjamin Recht

Author : Moritz Hardt,Benjamin Recht
Publisher : Princeton University Press
Page : 321 pages
File Size : 53,6 Mb
Release : 2022-08-23
Category : Computers
ISBN : 9780691233727

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Patterns, Predictions, and Actions: Foundations of Machine Learning by Moritz Hardt,Benjamin Recht Pdf

An authoritative, up-to-date graduate textbook on machine learning that highlights its historical context and societal impacts Patterns, Predictions, and Actions introduces graduate students to the essentials of machine learning while offering invaluable perspective on its history and social implications. Beginning with the foundations of decision making, Moritz Hardt and Benjamin Recht explain how representation, optimization, and generalization are the constituents of supervised learning. They go on to provide self-contained discussions of causality, the practice of causal inference, sequential decision making, and reinforcement learning, equipping readers with the concepts and tools they need to assess the consequences that may arise from acting on statistical decisions. Provides a modern introduction to machine learning, showing how data patterns support predictions and consequential actions Pays special attention to societal impacts and fairness in decision making Traces the development of machine learning from its origins to today Features a novel chapter on machine learning benchmarks and datasets Invites readers from all backgrounds, requiring some experience with probability, calculus, and linear algebra An essential textbook for students and a guide for researchers

PATTERNS, PREDICTIONS, AND ACTIONS

Moritz Hardt,Benjamin Recht

Author : Moritz Hardt,Benjamin Recht
Publisher : Learningbooks
Page : 0 pages
File Size : 44,9 Mb
Release : 2023-12-15
Category : Computers
ISBN : 9732348089

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PATTERNS, PREDICTIONS, AND ACTIONS by Moritz Hardt,Benjamin Recht Pdf

Dive into the captivating world of artificial intelligence and data-driven innovation with "Patterns, Predictions, and Actions: A Story about Machine Learning" by acclaimed authors Moritz Hardt and Benjamin Recht. This enthralling narrative unfolds like a carefully crafted algorithm, weaving together the threads of cutting-edge technology, human ingenuity, and the limitless possibilities of machine learning. Embark on a journey that unravels the intricate patterns hidden within vast datasets, as Hardt and Recht skillfully guide you through the labyrinth of algorithms and models. Immerse yourself in the language of data science, where every line of code tells a story, and every prediction holds the key to unlocking unprecedented insights. From regression analysis to deep neural networks, this book explores the diverse landscape of machine learning, offering readers a comprehensive understanding of the tools shaping the future. As you turn the pages, you'll witness the power of predictive analytics as it transcends industries, from finance to healthcare, and transforms the way we approach complex problems. The authors illuminate the synergy between man and machine, emphasizing how collaborative efforts between humans and algorithms can usher in a new era of technological advancement and societal progress. "Patterns, Predictions, and Actions" is not merely a book; it's a roadmap for the curious minds seeking to decipher the intricate dance between data and decisions. With each chapter, you'll discover how machine learning algorithms unravel patterns in chaos, predict future trends with uncanny accuracy, and ultimately empower us to take decisive actions that shape the world around us. This literary masterpiece is a treasure trove of knowledge for both the seasoned data scientist and the curious novice. Whether you're fascinated by the mathematical intricacies of machine learning or intrigued by its real-world applications, this book offers a rare blend of technical expertise and storytelling prowess. Uncover the secrets of machine learning, demystify the algorithms driving innovation, and embark on a journey that explores the intersection of human intuition and artificial intelligence. "Patterns, Predictions, and Actions" invites you to envision a future where the marriage of data and decision-making transforms not just industries, but the very fabric of our existence. Immerse yourself in this captivating narrative, and let the algorithms guide you through a story that is as profound as it is predictive.

Reinforcement Learning, second edition

Richard S. Sutton,Andrew G. Barto

Author : Richard S. Sutton,Andrew G. Barto
Publisher : MIT Press
Page : 549 pages
File Size : 54,9 Mb
Release : 2018-11-13
Category : Computers
ISBN : 9780262352703

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Reinforcement Learning, second edition by Richard S. Sutton,Andrew G. Barto Pdf

The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the first edition, this second edition focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes. Part I covers as much of reinforcement learning as possible without going beyond the tabular case for which exact solutions can be found. Many algorithms presented in this part are new to the second edition, including UCB, Expected Sarsa, and Double Learning. Part II extends these ideas to function approximation, with new sections on such topics as artificial neural networks and the Fourier basis, and offers expanded treatment of off-policy learning and policy-gradient methods. Part III has new chapters on reinforcement learning's relationships to psychology and neuroscience, as well as an updated case-studies chapter including AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. The final chapter discusses the future societal impacts of reinforcement learning.

Understanding Machine Learning

Shai Shalev-Shwartz,Shai Ben-David

Author : Shai Shalev-Shwartz,Shai Ben-David
Publisher : Cambridge University Press
Page : 415 pages
File Size : 42,8 Mb
Release : 2014-05-19
Category : Computers
ISBN : 9781107057135

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Understanding Machine Learning by Shai Shalev-Shwartz,Shai Ben-David Pdf

Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

Foundations of Machine Learning, second edition

Mehryar Mohri,Afshin Rostamizadeh,Ameet Talwalkar

Author : Mehryar Mohri,Afshin Rostamizadeh,Ameet Talwalkar
Publisher : MIT Press
Page : 505 pages
File Size : 42,6 Mb
Release : 2018-12-25
Category : Computers
ISBN : 9780262351362

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Foundations of Machine Learning, second edition by Mehryar Mohri,Afshin Rostamizadeh,Ameet Talwalkar Pdf

A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.

Fairness and Machine Learning

Solon Barocas,Moritz Hardt,Arvind Narayanan

Author : Solon Barocas,Moritz Hardt,Arvind Narayanan
Publisher : MIT Press
Page : 341 pages
File Size : 44,8 Mb
Release : 2023-12-19
Category : Computers
ISBN : 9780262376525

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Fairness and Machine Learning by Solon Barocas,Moritz Hardt,Arvind Narayanan Pdf

An introduction to the intellectual foundations and practical utility of the recent work on fairness and machine learning. Fairness and Machine Learning introduces advanced undergraduate and graduate students to the intellectual foundations of this recently emergent field, drawing on a diverse range of disciplinary perspectives to identify the opportunities and hazards of automated decision-making. It surveys the risks in many applications of machine learning and provides a review of an emerging set of proposed solutions, showing how even well-intentioned applications may give rise to objectionable results. It covers the statistical and causal measures used to evaluate the fairness of machine learning models as well as the procedural and substantive aspects of decision-making that are core to debates about fairness, including a review of legal and philosophical perspectives on discrimination. This incisive textbook prepares students of machine learning to do quantitative work on fairness while reflecting critically on its foundations and its practical utility. • Introduces the technical and normative foundations of fairness in automated decision-making • Covers the formal and computational methods for characterizing and addressing problems • Provides a critical assessment of their intellectual foundations and practical utility • Features rich pedagogy and extensive instructor resources

Pattern Recognition and Machine Learning

Christopher M. Bishop

Author : Christopher M. Bishop
Publisher : Springer
Page : 0 pages
File Size : 46,9 Mb
Release : 2016-08-23
Category : Computers
ISBN : 1493938436

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Pattern Recognition and Machine Learning by Christopher M. Bishop Pdf

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

An Introduction to Machine Learning

Gopinath Rebala,Ajay Ravi,Sanjay Churiwala

Author : Gopinath Rebala,Ajay Ravi,Sanjay Churiwala
Publisher : Springer
Page : 263 pages
File Size : 54,5 Mb
Release : 2019-05-07
Category : Technology & Engineering
ISBN : 9783030157296

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An Introduction to Machine Learning by Gopinath Rebala,Ajay Ravi,Sanjay Churiwala Pdf

Just like electricity, Machine Learning will revolutionize our life in many ways – some of which are not even conceivable today. This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms. Many of the mathematical concepts are explained in an intuitive manner. The book starts with an overview of machine learning and the underlying Mathematical and Statistical concepts before moving onto machine learning topics. It gradually builds up the depth, covering many of the present day machine learning algorithms, ending in Deep Learning and Reinforcement Learning algorithms. The book also covers some of the popular Machine Learning applications. The material in this book is agnostic to any specific programming language or hardware so that readers can try these concepts on whichever platforms they are already familiar with. Offers a comprehensive introduction to Machine Learning, while not assuming any prior knowledge of the topic; Provides a complete overview of available techniques and algorithms in conceptual terms, covering various application domains of machine learning; Not tied to any specific software language or hardware implementation.

Model-Based Machine Learning

John Winn

Author : John Winn
Publisher : CRC Press
Page : 469 pages
File Size : 42,8 Mb
Release : 2023-11-30
Category : Business & Economics
ISBN : 9781498756822

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Model-Based Machine Learning by John Winn Pdf

Today, machine learning is being applied to a growing variety of problems in a bewildering variety of domains. A fundamental challenge when using machine learning is connecting the abstract mathematics of a machine learning technique to a concrete, real world problem. This book tackles this challenge through model-based machine learning which focuses on understanding the assumptions encoded in a machine learning system and their corresponding impact on the behaviour of the system. The key ideas of model-based machine learning are introduced through a series of case studies involving real-world applications. Case studies play a central role because it is only in the context of applications that it makes sense to discuss modelling assumptions. Each chapter introduces one case study and works through step-by-step to solve it using a model-based approach. The aim is not just to explain machine learning methods, but also showcase how to create, debug, and evolve them to solve a problem. Features: Explores the assumptions being made by machine learning systems and the effect these assumptions have when the system is applied to concrete problems. Explains machine learning concepts as they arise in real-world case studies. Shows how to diagnose, understand and address problems with machine learning systems. Full source code available, allowing models and results to be reproduced and explored. Includes optional deep-dive sections with more mathematical details on inference algorithms for the interested reader.

Fairness and Machine Learning

Solon Barocas,Moritz Hardt,Arvind Narayanan

Author : Solon Barocas,Moritz Hardt,Arvind Narayanan
Publisher : MIT Press
Page : 341 pages
File Size : 40,9 Mb
Release : 2023-12-19
Category : Computers
ISBN : 9780262048613

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Fairness and Machine Learning by Solon Barocas,Moritz Hardt,Arvind Narayanan Pdf

An introduction to the intellectual foundations and practical utility of the recent work on fairness and machine learning. Fairness and Machine Learning introduces advanced undergraduate and graduate students to the intellectual foundations of this recently emergent field, drawing on a diverse range of disciplinary perspectives to identify the opportunities and hazards of automated decision-making. It surveys the risks in many applications of machine learning and provides a review of an emerging set of proposed solutions, showing how even well-intentioned applications may give rise to objectionable results. It covers the statistical and causal measures used to evaluate the fairness of machine learning models as well as the procedural and substantive aspects of decision-making that are core to debates about fairness, including a review of legal and philosophical perspectives on discrimination. This incisive textbook prepares students of machine learning to do quantitative work on fairness while reflecting critically on its foundations and its practical utility. • Introduces the technical and normative foundations of fairness in automated decision-making • Covers the formal and computational methods for characterizing and addressing problems • Provides a critical assessment of their intellectual foundations and practical utility • Features rich pedagogy and extensive instructor resources

Learning to Play

Aske Plaat

Author : Aske Plaat
Publisher : Springer Nature
Page : 330 pages
File Size : 52,6 Mb
Release : 2020-12-23
Category : Computers
ISBN : 9783030592387

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Learning to Play by Aske Plaat Pdf

In this textbook the author takes as inspiration recent breakthroughs in game playing to explain how and why deep reinforcement learning works. In particular he shows why two-person games of tactics and strategy fascinate scientists, programmers, and game enthusiasts and unite them in a common goal: to create artificial intelligence (AI). After an introduction to the core concepts, environment, and communities of intelligence and games, the book is organized into chapters on reinforcement learning, heuristic planning, adaptive sampling, function approximation, and self-play. The author takes a hands-on approach throughout, with Python code examples and exercises that help the reader understand how AI learns to play. He also supports the main text with detailed pointers to online machine learning frameworks, technical details for AlphaGo, notes on how to play and program Go and chess, and a comprehensive bibliography. The content is class-tested and suitable for advanced undergraduate and graduate courses on artificial intelligence and games. It's also appropriate for self-study by professionals engaged with applications of machine learning and with games development. Finally it's valuable for any reader engaged with the philosophical implications of artificial and general intelligence, games represent a modern Turing test of the power and limitations of AI.

Pattern Recognition and Machine Learning

Y. Anzai

Author : Y. Anzai
Publisher : Morgan Kaufmann
Page : 428 pages
File Size : 52,7 Mb
Release : 1992-07-14
Category : Computers
ISBN : 0120588307

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Pattern Recognition and Machine Learning by Y. Anzai Pdf

Recognition and learning by a computer. Representing information. Generation and transformation of representations. Pattern feature extraction. Pattern understanding methods. Learning concepts. Learning procedures. Learning based on logic. Learning by classification and discovery. Learning by neural networks.

Intelligent Optimization Techniques for Business Analytics

Bansal, Sanjeev,Kumar, Nitendra,Agarwal, Priyanka

Author : Bansal, Sanjeev,Kumar, Nitendra,Agarwal, Priyanka
Publisher : IGI Global
Page : 377 pages
File Size : 53,6 Mb
Release : 2024-04-15
Category : Business & Economics
ISBN : 9798369315996

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Intelligent Optimization Techniques for Business Analytics by Bansal, Sanjeev,Kumar, Nitendra,Agarwal, Priyanka Pdf

Today, the convergence of cutting-edge algorithms and actionable insights in business is paramount for success. Scholars and practitioners grapple with the dilemma of optimizing data to drive efficiency, innovation, and competitiveness. The formidable challenge of effectively harnessing the immense power of intelligent optimization techniques and business analytics only increases as the volume of data grows exponentially, and the complexities of navigating the intricate landscape of business analytics becomes more daunting. This pressing issue underscores the critical need for a comprehensive solution, and Intelligent Optimization Techniques for Business Analytics is poised to provide much-needed answers. This groundbreaking book offers an all-encompassing solution to the challenges that academic scholars encounter in the pursuit of mastering the interplay between learning algorithms and intelligent optimization techniques for business analytics. Through a wealth of diverse perspectives and expert case studies, it illuminates the path to effectively implementing these advanced systems in real-world business scenarios. It caters not only to the scholarly community but also to industry professionals and policymakers, equipping them with the necessary tools and insights to excel in the realm of data-driven decision-making.

Deep Reinforcement Learning in Action

Alexander Zai,Brandon Brown

Author : Alexander Zai,Brandon Brown
Publisher : Manning Publications
Page : 381 pages
File Size : 45,6 Mb
Release : 2020-04-28
Category : Computers
ISBN : 9781617295430

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Deep Reinforcement Learning in Action by Alexander Zai,Brandon Brown Pdf

Summary Humans learn best from feedback—we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences. This reinforcement process can be applied to computer programs allowing them to solve more complex problems that classical programming cannot. Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along with the practical skills and techniques you’ll need to implement it into your own projects. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Deep reinforcement learning AI systems rapidly adapt to new environments, a vast improvement over standard neural networks. A DRL agent learns like people do, taking in raw data such as sensor input and refining its responses and predictions through trial and error. About the book Deep Reinforcement Learning in Action teaches you how to program AI agents that adapt and improve based on direct feedback from their environment. In this example-rich tutorial, you’ll master foundational and advanced DRL techniques by taking on interesting challenges like navigating a maze and playing video games. Along the way, you’ll work with core algorithms, including deep Q-networks and policy gradients, along with industry-standard tools like PyTorch and OpenAI Gym. What's inside Building and training DRL networks The most popular DRL algorithms for learning and problem solving Evolutionary algorithms for curiosity and multi-agent learning All examples available as Jupyter Notebooks About the reader For readers with intermediate skills in Python and deep learning. About the author Alexander Zai is a machine learning engineer at Amazon AI. Brandon Brown is a machine learning and data analysis blogger. Table of Contents PART 1 - FOUNDATIONS 1. What is reinforcement learning? 2. Modeling reinforcement learning problems: Markov decision processes 3. Predicting the best states and actions: Deep Q-networks 4. Learning to pick the best policy: Policy gradient methods 5. Tackling more complex problems with actor-critic methods PART 2 - ABOVE AND BEYOND 6. Alternative optimization methods: Evolutionary algorithms 7. Distributional DQN: Getting the full story 8.Curiosity-driven exploration 9. Multi-agent reinforcement learning 10. Interpretable reinforcement learning: Attention and relational models 11. In conclusion: A review and roadmap

Machine Learning and Data Science Blueprints for Finance

Hariom Tatsat,Sahil Puri,Brad Lookabaugh

Author : Hariom Tatsat,Sahil Puri,Brad Lookabaugh
Publisher : "O'Reilly Media, Inc."
Page : 432 pages
File Size : 45,6 Mb
Release : 2020-10-01
Category : Computers
ISBN : 9781492073000

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Machine Learning and Data Science Blueprints for Finance by Hariom Tatsat,Sahil Puri,Brad Lookabaugh Pdf

Over the next few decades, machine learning and data science will transform the finance industry. With this practical book, analysts, traders, researchers, and developers will learn how to build machine learning algorithms crucial to the industry. You’ll examine ML concepts and over 20 case studies in supervised, unsupervised, and reinforcement learning, along with natural language processing (NLP). Ideal for professionals working at hedge funds, investment and retail banks, and fintech firms, this book also delves deep into portfolio management, algorithmic trading, derivative pricing, fraud detection, asset price prediction, sentiment analysis, and chatbot development. You’ll explore real-life problems faced by practitioners and learn scientifically sound solutions supported by code and examples. This book covers: Supervised learning regression-based models for trading strategies, derivative pricing, and portfolio management Supervised learning classification-based models for credit default risk prediction, fraud detection, and trading strategies Dimensionality reduction techniques with case studies in portfolio management, trading strategy, and yield curve construction Algorithms and clustering techniques for finding similar objects, with case studies in trading strategies and portfolio management Reinforcement learning models and techniques used for building trading strategies, derivatives hedging, and portfolio management NLP techniques using Python libraries such as NLTK and scikit-learn for transforming text into meaningful representations

Patterns Predictions And Actions Foundations Of Machine Learning

Patterns, Predictions, and Actions: Foundations of Machine Learning by Moritz Hardt,Benjamin Recht Pdf

PATTERNS, PREDICTIONS, AND ACTIONS by Moritz Hardt,Benjamin Recht Pdf

Reinforcement Learning, second edition by Richard S. Sutton,Andrew G. Barto Pdf

Understanding Machine Learning by Shai Shalev-Shwartz,Shai Ben-David Pdf

Foundations of Machine Learning, second edition by Mehryar Mohri,Afshin Rostamizadeh,Ameet Talwalkar Pdf

Fairness and Machine Learning by Solon Barocas,Moritz Hardt,Arvind Narayanan Pdf

Pattern Recognition and Machine Learning by Christopher M. Bishop Pdf

An Introduction to Machine Learning by Gopinath Rebala,Ajay Ravi,Sanjay Churiwala Pdf

Model-Based Machine Learning by John Winn Pdf

Fairness and Machine Learning by Solon Barocas,Moritz Hardt,Arvind Narayanan Pdf

Learning to Play by Aske Plaat Pdf

Pattern Recognition and Machine Learning by Y. Anzai Pdf

Intelligent Optimization Techniques for Business Analytics by Bansal, Sanjeev,Kumar, Nitendra,Agarwal, Priyanka Pdf

Deep Reinforcement Learning in Action by Alexander Zai,Brandon Brown Pdf

Machine Learning and Data Science Blueprints for Finance by Hariom Tatsat,Sahil Puri,Brad Lookabaugh Pdf

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