Foundations of Machine Learning, 2nd Edition
|Price||$54.38 - $68.05
|Authors||Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar|
|Format||Paper book / ebook (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 2nd 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.
4 5 87
by Sebastian Raschka, Vahid Mirjalili
Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems.Packed with clear explanations, visualizations, and working examples, the book covers all the...
Price: $35.99 | Publisher: Packt Publishing | Release: 2019
by Roger Barga, Valentine Fontama, Wee Hyong Tok
Predictive Analytics with Microsoft Azure Machine Learning, Second Edition is a practical tutorial introduction to the field of data science and machine learning, with a focus on building and deploying predictive models. The book provides a thorough overview of the Microsoft Azure Machine Learning service released for general availability...
Price: $38.04 | Publisher: Apress | Release: 2015
by Miroslav Kubat
This book presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, n...
Price: $65.87 | Publisher: Springer | Release: 2017
by Giuseppe Bonaccorso
Mastering Machine Learning Algorithms, 2nd Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervi...
Price: $40.49 | Publisher: Packt Publishing | Release: 2020
by Karthik Ramasubramanian, Abhishek Singh
Examine the latest technological advancements in building a scalable machine-learning model with big data using R. This second edition shows you how to work with a machine-learning algorithm and use it to build a ML model from raw data. You will see how to use R programming with TensorFlow, thus avoiding the effort of learning Python if y...
Price: $31.74 | Publisher: Apress | Release: 2019
by Santanu Ganguly
Know how to adapt quantum computing and machine learning algorithms. This book takes you on a journey into hands-on quantum machine learning (QML) through various options available in industry and research.The first three chapters offer insights into the combination of the science of quantum mechanics and the techniques of machine learnin...
Price: $44.74 | Publisher: Apress | Release: 2021
by James G. Robertson, Bill Fitzgerald
As social networks become more popular, their role in the classroom has come under scrutiny. Drupal offers a wide variety of useful tools for educators. Within a single Drupal site, you can set up social bookmarking, podcasting, video hosting, formal and informal groups, rich user profiles, and other features commonly associated with soci...
Price: $32.99 | Publisher: Packt Publishing | Release: 2013
by Nikhil Buduma
With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research, one that's paving the way for modern machine learning. In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field.Companies su...
Price: $30.45 | Publisher: O'Reilly Media | Release: 2017