The first comprehensive introduction to Multi-Agent Reinforcement Learning (MARL), covering MARL’s models, solution concepts, algorithmic ideas, technical challenges, and modern approaches.

This book shows them how to assess it in the context of the business’s goals, reframe it to work optimally for both the data scientist and the employer, and then execute on it. Packed with real-world examples that take you from start to finish.

This book explains to you how to make (supervised) machine learning models interpretable. The book focuses on machine learning models for tabular data (also called relational or structured data) and less on computer vision and NLP tasks.

The first comprehensive introduction to Multi-Agent Reinforcement Learning (MARL), covering MARL’s models, solution concepts, algorithmic ideas, technical challenges, and modern approaches.

Distributional reinforcement learning is a new mathematical formalism for thinking about decisions. This first comprehensive guide provides a new mathematical formalism for thinking about decisions from a probabilistic perspective.

This self-contained textbook introduces students and researchers of AI to the main mathematical techniques used to analyze machine learning algorithms, with motivations and applications.

This book takes you on an enlightening journey through the fascinating world of machine learning, helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs.

This book is an introduction 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.

Everything you really need to know in Machine Learning in a hundred pages! This book provides a great practical guide to get started and execute on ML within a few days without necessarily knowing much about ML apriori.

This book covers the same materials as Introduction to Statistical Learning: with Applications in R (ISLR) but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.

This book is a collection of lecture notes that offers an accessible introduction to Neural Networks and machine learning in general. The focus lies on classical machine learning techniques, with a bias towards classification and regression.

This book is designed for both financial economists interested in grasping machine learning tools, as well as for statisticians and machine learners seeking interesting financial contexts where advanced methods may be deployed.

How the use of machine learning to analyze art images has revived formalism in art history, presenting a golden opportunity for art historians and computer scientists to learn from one another.

It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering.

Understanding and trusting models and their results is a hallmark of good science. Get an applied perspective on how this applies to machine learning, including fairness, accountability, transparency, and explainable AI.

This practical guide provides helps to solve machine learning challenges you may encounter in your work. Go beyond theory and concepts by learning the nuts and bolts you need to construct working machine learning applications.

This open access book provides a wealth of hands-on examples that illustrate how hyperparameter tuning can be applied in practice and gives deep insights into the working mechanisms of machine learning (ML) and deep learning (DL) methods.

This book is for people who have some theoretical knowledge of machine learning and deep learning and want to dive into applied machine learning. The book is best enjoyed with a cup of coffee and a laptop/workstation where you can code along.

This book provides a self-contained introduction to Quantum Machine Learning for an audience of engineers with a background in probability and linear algebra, describes the necessary background, concepts, and tools, covers parametrized quantum circuits, etc.

This self contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It provides a beautiful exposition of the mathematics underpinning modern machine learning.

Covering everything you need to know about machine learning, now you can master the mathematics, computer science and statistics behind this field and develop your very own neural networks!

This book offers a comprehensive and thorough introduction to almost all aspects of metalearning and Automated Machine Learning (AutoML). It can help developers to develop systems that can improve themselves through experience.

Deep reinforcement learning is the combination of reinforcement learning (RL) and deep learning. This field of research has recently been able to solve a wide range of complex decision-making tasks that were previously out of reach for a machine.

This book is a comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. It is written in an informal, accessible style, complete with pseudo-code for the most important algorithms.

This book expands the scope of Machine Learning to encompass more challenging problems, discusses methods for discovering 'insights' about data, and how to use probabilistic models for causal inference and decision making under uncertainty.

This how-to reference guide provides everything you need - including code samples and notebooks - to start putting Machine Learning to work. It's a collection of technical blogs from industry thought leaders with practical use cases you can leverage today.

This is a collection of (mostly) pen-and-paper exercises in machine learning. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning.

This modern and self-contained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. It provides comprehensive coverage of neural networks, their evolution, their structure, their applications, etc.

This book covers the building blocks of the most common methods in machine learning. This set of methods is like a toolbox for machine learning engineers. Those entering the field of machine learning should feel comfortable with this toolbox.

This book is a general introduction to machine learning. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms.

This book is a primer on machine learning for programmers trying to get up to speed quickly. You'll learn how machine learning works and how to apply it in practice. Focus on just a few powerful models (algorithms) that are extremely effective on real problems,

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.

This book provides a long-needed systematic and unified treatment of theoretical and practical aspects of Gaussian Processes (GPs) in machine learning. It deals with the supervised-learning problem for both regression and classification.

This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the both biometrics and media forensics fields.

Explains the principles behind the automated learning approach and the considerations underlying its usage. Provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations.

In this book you will learn how to align on ML strategies in a team setting, as well as how to set up development (dev) sets and test sets. After finishing this book, you will have a deep understanding of how to set technical direction for a machine learning project.

This book aims to present the statistical foundations of machine learning intended as the discipline which deals with the automatic design of models from data. All the examples are implemented in the statistical programming language R.

Illustrates the fundamental concepts that link statistics and machine learning, so that the reader can not only employ statistical and machine learning models using modern Python modules, but also understand their relative strengths and weaknesses.

It provides an accessible overview of the field of Statistical Learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years.

This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics.

This book explores machine learning and its impact on how we make sense of the world. It introduces readers to the key concepts of machine learning, discusses the potential applications and limitations of predictions generated by machines using data.

This book bridges theoretical computer science and machine learning by exploring what the two sides can teach each other. It emphasizes the need for flexible, tractable models that better capture not what makes machine learning hard, but what makes it easy.

This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics. The main parts of the book include exploratory data analysis, pattern mining, clustering, and classification.

This book 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. This learning ability is one of the hallmarks of human intelligence.

It provides a clear and simple account of the key ideas and algorithms of reinforcement learning that is accessible to readers in all the related disciplines. Focuses on core online learning algorithms, with the more mathematical material set off in shaded boxes.

The most complete applied AI book out there. It is filled with best practices and design patterns of building reliable machine learning solutions that scale. It embraces the most important thing you need to know about machine learning: mistakes are possible.

This book aims at providing an introduction to key concepts, algorithms, and theoretical frameworks in machine learning, including supervised and unsupervised learning, statistical learning theory, probabilistic graphical models and approximate inference.

This comprehensive textbook presents basic machine learning methods for civil engineers who do not have a specialized background in statistics or in computer science. It includes several case studies that students and professionals will appreciate.

This book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems.

This book tries to equip the developers of today and tomorrow with tools they can use to better understand, evaluate, and shape machine learning. If you know some Python and you want to use machine learning and deep learning, pick up this book.

This is a set of introductory materials that covers most major aspects of modern machine learning (supervised learning, unsupervised learning, large margin methods, probabilistic modeling, learning theory, etc.).

This book gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python. You'll learn the basics by working with classic prediction, classification, and clustering algorithms.

This book presents a robust machine learning approach that contains elements of mathematical optimization, game theory, and learning theory: an optimization method that learns from experience as more aspects of the problem are observed.

This book covers an introduction to convex optimization, one of the powerful and tractable optimization problems that can be efficiently solved on a computer. The goal is to help develop a sense of what convex optimization is, and how it can be used.

This book teaches everyone to build an AI to work in their applications. Once you've read this book, you're only limited by your imagination. It gives you everything you need to build AI systems with reinforcement learning and deep learning.

Provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms, covers a wide array of topics that have not been addressed by other books.

This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. It is written in an informal, accessible style, complete with pseudo-code for the most important algorithms.

This book focuses on theoretical aspects of Statistical Learning and Sequential Prediction, a unified approach to analyzing learning in both scenarios, brings together ideas from probability and statistics, game theory, algorithms, and optimization.

This book presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. Typical Cyber Physical Systems applications are condition monitoring, predictive maintenance, image processing and diagnosis.

This book focuses on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. It gives a fairly comprehensive catalog of learning problems, describe the core ideas, etc.

The purpose of the book is to consider large and challenging multistage decision problems, which can be solved in principle by dynamic programming and optimal control, but their exact solution is computationally intractable.

It weaves together the theoretical exposition, design principles, and practical applications of efficient machine learning, aims to equip engineers, students of engineering, and system designers to design and create new and more efficient machine learning systems.

This practical introduction is ideally suited to computer scientists without a background in calculus and linear algebra. You'll develop analytical and problem-solving skills that equip them for the real world. Numerous examples and exercises are provided.

This book is a introductory textbook on the subject, discussesing many methods from different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, and data mining.

This book describes an open machine learning architecture. Including key aspects that are involved for real business use. It focuses on Free and Open Source Software (FOSS) machine learning software and open datasets.

This book, written by the inventors of the Boosting method, brings together, organizes, simplifies, and substantially extends two decades of research on boosting, presenting both theory and applications in a way that is accessible to readers from diverse backgrounds.

A practical guide to working with cognitive APIs developed by Microsoft and provided with the Azure platform to developers and businesses. It delivers an accessible guide to integrating computer vision, decision-making, speech, and more into your apps.

This book guides readers through the building blocks of Support Vector Machines (SVMs), from basic concepts to crucial problem-solving algorithms. It also includes numerous code examples and a lengthy bibliography for further study.

This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties.

This book will be exploring machine learning, the concepts that run these technologies and by the time you get to the end you will have more knowledge than many and will be equipped to start building your own applications.

This book presents algorithms and techniques used in data stream mining and real-time analytics. Taking a hands-on approach, it demonstrates the techniques using MOA (Massive Online Analysis), a popular, freely available open-source software framework, allowing readers to try out the techniques after reading the explanations.

This is an introduction to the main issues associated with the basics of machine learning and the algorithms used in data mining. It offers a grounding in machine learning concepts as well as practical advice on techniques in real-world data mining.

Statistical, machine learning and neural network approaches to classification are all covered in this book to provide an objective assessment of the potential for machine learning algorithms in solving significant commercial and industrial problems, widening the foundation for exploitation of these and related algorithms.

Written by three experts, this is the only comprehensive book on the subject. It offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning.

This book not only introduces you to contemporary machine learning systems, but also provides a conceptual framework to help you integrate machine-learning capabilities into your user-facing designs, using tangible, real-world examples.

This exclusive report unpacks concepts and innovations that represent the frontiers of ever-smarter machines. You’ll get a rare glimpse into this exciting field through the eyes of some of its leading minds.

The tutorials presented here will introduce you to some of the most important deep learning algorithms and will also show you how to run them using Theano. Theano is a python library that makes writing deep learning models easy, and gives the option of training them on a GPU.

This open access book provides a comprehensive overview of the state of the art in research and applications of Foundation Models and is intended for readers familiar with basic Natural Language Processing (NLP) concepts.

This text takes an empirical approach to the subject, based on applying statistical and other machine-learning algorithms to large corporations. It describes a unified vision of speech and language processing. Emphasis is on practical and scientific applications.

This book provides an up-to-date and systematical introduction to the principles and algorithms of machine learning, as well as a good introduction to many approaches of machine learning, and the source of useful bibliographical information.

Present the latest applications of machine learning, which mainly include: speech recognition, traffic and fault classification, surface quality prediction in laser machining, network security and bioinformatics, enterprise credit risk evaluation, and so on.

This book presents a new approach to numerical analysis for modern computer scientists, covers a wide range of topics - from numerical linear algebra to optimization and differential equations - focusing on real-world motivation and unifying themes.

This introductory book looks at all aspects of Machine Translation: covering questions of what it is like to use a modern Machine Translation system, through questions about how it is done, to questions of evaluating systems, and what developments can be foreseen.

This book is an introduction to inductive logic programming (ILP), which aims at a formal framework as well as practical algorithms for inductively learning relational descriptions in the form of logic programs.

This book discusses the motivations for and principles of learning algorithms for deep architectures. By analyzing and comparing recent results with different learning algorithms for deep architectures, explanations for their success are proposed.

Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you the core concepts behind neural networks and deep learning.

This books reports the latest developments and futuristic trends in Machine Learning. It involves broad topics including Fuzzy Logic, Neural Networks (NNs), Evolutionary Algorithms (EAs), Probability and Statistics, Decision Trees, etc.

This book outlines a methodology for deploying artificial intelligence and machine learning to enhance historical research. It presents the theory behind this methodology from a humanities perspective and discusses its practical applications.

This book shows several different approaches to applying machine learning to data analysis and prediction problems. It also demonstrates different clustering and classification techniques, and explains how effective these techniques can be.

The book starts from the basics of probability and model fitting and works up to real examples that the reader can implement and modify to build useful vision systems. The detailed methodological presentation is useful for practitioners of computer vision.

It gives a focused introduction to the primary themes in a Robot learning course and demonstrates the relevance and practicality of various Machine Learning algorithms to a wide variety of real-world applications from evolutionary techniques to reinforcement learning, classification, control, uncertainty and many other important fields.

This book presents today's state and development tendencies of machine learning. Taking into account the large amount of knowledge about machine learning and practice presented in the book, it is divided into three major parts: Introduction, Machine Learning Theory and Applications.

The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms.

This textbook introduces Information theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction.

Illustrateing how to program AI algorithms in Lisp, Prolog, and Java. Topics include: simple production-like system based on logic, logic-based learning, and natural language parsing.

This book provides a broad overview on face recognition and identified trends for future developments and the means for implementing robust systems..

This book introduces embodied cognitive science and illustrates its foundational ideas through the construction and observation of LEGO Mindstorms robots.

Intended to introduce the foundations of cognitive science, this book addresses a number of questions currently being asked by those practicing in the field of cognitive science. It highlights the fundamental tensions and lines of fragmentation of cognitive science.