Foundations of Machine Learning, 2nd Edition
Price  $54.38  $68.05

eBook  Free 
Rating  
Authors  Mehryar Mohri, Afshin Rostamizadeh, Ameet Talwalkar 
Publisher  Selfpublishing 
Published  2018 
Pages  505 
Language  English 
Format  Paper book / ebook (PDF) 
ISBN10  0262039400 
ISBN13  9780262039406 
A new edition of a graduatelevel 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 selfcontained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VCdimension; Support Vector Machines (SVMs); kernel methods; boosting; online learning; multiclass 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.
 Mehryar Mohri
 Afshin Rostamizadeh
 Ameet Talwalkar
4 5 87
Similar Books
Fundamentals of Deep Learning, 2nd Edition
by Nithin Buduma, Nikhil Buduma, Joe Papa
We're in the midst of an AI research explosion. Deep learning has unlocked superhuman perception to power our push toward creating selfdriving vehicles, defeating human experts at a variety of difficult games including Go, and even generating essays with shockingly coherent prose. But deciphering these breakthroughs often takes a Ph...
Price: $52.95  Publisher: O'Reilly Media  Release: 2022
Python Machine Learning, 3rd Edition
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 stepbystep 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
Mastering Azure Machine Learning, 2nd Edition
by Christoph Korner, Marcel Alsdorf
Azure Machine Learning is a cloud service for accelerating and managing the machine learning (ML) project life cycle that ML professionals, data scientists, and engineers can use in their daytoday workflows. This book covers the endtoend ML process using Microsoft Azure Machine Learning, including data preparation, performing and logg...
Price: $44.99  Publisher: Packt Publishing  Release: 2022
Predictive Analytics with Microsoft Azure Machine Learning, 2nd Edition
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
An Introduction to Machine Learning, 2nd Edition
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, nearestneighbor classifiers, linear and polynomial classifiers, decision trees, n...
Price: $65.87  Publisher: Springer  Release: 2017
Mastering Machine Learning Algorithms, 2nd Edition
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 semisupervised learning, reinforcement learning, supervi...
Price: $40.49  Publisher: Packt Publishing  Release: 2020
Machine Learning Using R, 2nd Edition
by Karthik Ramasubramanian, Abhishek Singh
Examine the latest technological advancements in building a scalable machinelearning model with big data using R. This second edition shows you how to work with a machinelearning 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
Quantum Machine Learning: An Applied Approach
by Santanu Ganguly
Know how to adapt quantum computing and machine learning algorithms. This book takes you on a journey into handson 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