Information Driven Machine Learning
Download Information Driven Machine Learning full books in PDF, epub, and Kindle. Read online free Information Driven Machine Learning ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
Author | : Steven L. Brunton |
Publisher | : Cambridge University Press |
Total Pages | : 615 |
Release | : 2022-05-05 |
Genre | : Computers |
ISBN | : 1009098489 |
A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
Author | : Steven Finlay |
Publisher | : Relativistic |
Total Pages | : 194 |
Release | : 2018-07 |
Genre | : |
ISBN | : 9781999730345 |
Artificial Intelligence (AI) and Machine Learning are now mainstream business tools. They are being applied across many industries to increase profits, reduce costs, save lives and improve customer experiences. Organizations which understand these tools and know how to use them are benefiting at the expense of their rivals. Artificial Intelligence and Machine Learning for Business cuts through the hype and technical jargon that is often associated with these subjects. It delivers a simple and concise introduction for managers and business people. The focus is very much on practical application and how to work with technical specialists (data scientists) to maximize the benefits of these technologies. This third edition has been substantially revised and updated. It contains several new chapters and covers a broader set of topics than before, but retains the no-nonsense style of the original.
Author | : John Winn |
Publisher | : CRC Press |
Total Pages | : 469 |
Release | : 2023-11-30 |
Genre | : Business & Economics |
ISBN | : 1498756824 |
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.
Author | : John D. Kelleher |
Publisher | : MIT Press |
Total Pages | : 853 |
Release | : 2020-10-20 |
Genre | : Computers |
ISBN | : 0262361108 |
The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.
Author | : Concha Bielza |
Publisher | : Cambridge University Press |
Total Pages | : 709 |
Release | : 2020-11-26 |
Genre | : Computers |
ISBN | : 110849370X |
Trains researchers and graduate students in state-of-the-art statistical and machine learning methods to build models with real-world data.
Author | : Zhiyuan Sun |
Publisher | : Springer Nature |
Total Pages | : 187 |
Release | : 2022-06-01 |
Genre | : Computers |
ISBN | : 3031015819 |
Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past helps us learn new things with little data or effort. Lifelong learning aims to emulate this capability, because without it, an AI system cannot be considered truly intelligent. Research in lifelong learning has developed significantly in the relatively short time since the first edition of this book was published. The purpose of this second edition is to expand the definition of lifelong learning, update the content of several chapters, and add a new chapter about continual learning in deep neural networks—which has been actively researched over the past two or three years. A few chapters have also been reorganized to make each of them more coherent for the reader. Moreover, the authors want to propose a unified framework for the research area. Currently, there are several research topics in machine learning that are closely related to lifelong learning—most notably, multi-task learning, transfer learning, and meta-learning—because they also employ the idea of knowledge sharing and transfer. This book brings all these topics under one roof and discusses their similarities and differences. Its goal is to introduce this emerging machine learning paradigm and present a comprehensive survey and review of the important research results and latest ideas in the area. This book is thus suitable for students, researchers, and practitioners who are interested in machine learning, data mining, natural language processing, or pattern recognition. Lecturers can readily use the book for courses in any of these related fields.
Author | : Gerald Friedland |
Publisher | : Springer Nature |
Total Pages | : 281 |
Release | : 2024-01-02 |
Genre | : Computers |
ISBN | : 3031394771 |
This groundbreaking book transcends traditional machine learning approaches by introducing information measurement methodologies that revolutionize the field. Stemming from a UC Berkeley seminar on experimental design for machine learning tasks, these techniques aim to overcome the 'black box' approach of machine learning by reducing conjectures such as magic numbers (hyper-parameters) or model-type bias. Information-based machine learning enables data quality measurements, a priori task complexity estimations, and reproducible design of data science experiments. The benefits include significant size reduction, increased explainability, and enhanced resilience of models, all contributing to advancing the discipline's robustness and credibility. While bridging the gap between machine learning and disciplines such as physics, information theory, and computer engineering, this textbook maintains an accessible and comprehensive style, making complex topics digestible for a broad readership. Information-Driven Machine Learning explores the synergistic harmony among these disciplines to enhance our understanding of data science modeling. Instead of solely focusing on the "how," this text provides answers to the "why" questions that permeate the field, shedding light on the underlying principles of machine learning processes and their practical implications. By advocating for systematic methodologies grounded in fundamental principles, this book challenges industry practices that have often evolved from ideologic or profit-driven motivations. It addresses a range of topics, including deep learning, data drift, and MLOps, using fundamental principles such as entropy, capacity, and high dimensionality. Ideal for both academia and industry professionals, this textbook serves as a valuable tool for those seeking to deepen their understanding of data science as an engineering discipline. Its thought-provoking content stimulates intellectual curiosity and caters to readers who desire more than just code or ready-made formulas. The text invites readers to explore beyond conventional viewpoints, offering an alternative perspective that promotes a big-picture view for integrating theory with practice. Suitable for upper undergraduate or graduate-level courses, this book can also benefit practicing engineers and scientists in various disciplines by enhancing their understanding of modeling and improving data measurement effectively.
Author | : Bradley Efron |
Publisher | : Cambridge University Press |
Total Pages | : 496 |
Release | : 2016-07-21 |
Genre | : Mathematics |
ISBN | : 1108107958 |
The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.
Author | : Matthew Kirk |
Publisher | : "O'Reilly Media, Inc." |
Total Pages | : 253 |
Release | : 2014-09-26 |
Genre | : Computers |
ISBN | : 1449374093 |
Learn how to apply test-driven development (TDD) to machine-learning algorithms—and catch mistakes that could sink your analysis. In this practical guide, author Matthew Kirk takes you through the principles of TDD and machine learning, and shows you how to apply TDD to several machine-learning algorithms, including Naive Bayesian classifiers and Neural Networks. Machine-learning algorithms often have tests baked in, but they can’t account for human errors in coding. Rather than blindly rely on machine-learning results as many researchers have, you can mitigate the risk of errors with TDD and write clean, stable machine-learning code. If you’re familiar with Ruby 2.1, you’re ready to start. Apply TDD to write and run tests before you start coding Learn the best uses and tradeoffs of eight machine learning algorithms Use real-world examples to test each algorithm through engaging, hands-on exercises Understand the similarities between TDD and the scientific method for validating solutions Be aware of the risks of machine learning, such as underfitting and overfitting data Explore techniques for improving your machine-learning models or data extraction
Author | : Martin Wass |
Publisher | : |
Total Pages | : 240 |
Release | : 2018-06-06 |
Genre | : |
ISBN | : 9781983059018 |
The marketing industry is being disrupted right before of our eyes, and the new technological revolution will transform our world. Artificial intelligence is set to change everything we know about marketing and how we interact with customers. Business leaders need to pay attention, or they risk being left behind. Artificial intelligence will drive a massive shift in business models, and will profoundly change how businesses and customers communicate. Staying at the forefront of these changes is essential for any business to stay competitive. Data-Driven Marketing with Artificial Intelligence is the definitive guide to understanding and using AI in marketing. It is essential reading for corporate and marketing leaders, and anyone seeking to understand how artificial intelligence will lead us into the world of tomorrow. Featuring discussions with dozens of industry leaders, it provides both an overview of how AI will continue to affect online marketing, and details of how to implement these new tools into your business. Learn how traditional marketing strategies are being replaced by autonomous, data-driven, and self-optimizing systems capable of providing more relevance to each customer, improving loyalty, and ultimately increasing the bottom line. After reading this book, you will understand these key topics: * The disruption that artificial intelligence and other emerging technologies will have on marketing, sales, and the industries that surround them * The latest AI-based software tools and what they can do for marketers today * Using big data, predictive analytics, and machine learning in marketing * How to develop and implement your own custom AI software * The risks AI hold for your job or business * How new technologies, beyond AI, will disrupt marketing even further * The legal and ethical aspects of using artificial intelligence systems Leading experts and technology CEOs believe that soon, nearly every decision we make will be influenced by AI technology. Marketing is no exception, and it will see changes faster and with wider adoption than any other. In an AI-enabled world, companies will have to adjust to new purchase patterns to stay in business. Learning about and implementing AI tools will keep your business on the forefront of the next technological revolution--while the rest play catch up. Buy the book now to jump into the world of artificial intelligence and stay one step ahead of the competition!