Machine learning

An accessible undergraduate introduction to the concepts and methods in pattern recognition, machine learning and deep learning.

Now in its third edition, this book focuses on practical algorithms for mining data from even the largest datasets.

A groundbreaking introduction to vectors, matrices, and least squares for engineering applications, offering a wealth of practical examples.

This user guide presents a popular smoothing tool with practical applications in machine learning, engineering, and statistics.

An introductory textbook offering a low barrier entry to data science; the hands-on approach will appeal to students from a range of disciplines.

New to the second edition of this advanced text are several chapters on regression, including neural networks and deep learning.

Colorful example-rich introduction to the state-of-the-art for students in data science, as well as researchers and practitioners.

An intuitive and accessible textbook covering both fundamentals and applications.

A bold retooling of statistics to focus directly on predictive performance with traditional and contemporary data types and methodologies.

This user guide presents a popular smoothing tool with practical applications in machine learning, engineering, and statistics.

The first unified treatment of the interface between information theory and emerging topics in data science.

Surveys major topics in the epistemology of big data based scientific approaches and defends an inductivist view of these approaches.

A bold retooling of statistics to focus directly on predictive performance with traditional and contemporary data types and methodologies.

Provides a comprehensive textbook covering theory and practical examples for a course on data mining and data warehousing.

This integrated introduction to fundamentals, computation, and software is your key to understanding and using advanced Bayesian methods.

This integrated introduction to fundamentals, computation, and software is your key to understanding and using advanced Bayesian methods.

A coherent introductory text from a groundbreaking researcher, focusing on clarity and motivation to build intuition and understanding.

A practical guide for data scientists who want to improve the performance of any machine learning solution with feature engineering.

A coherent introductory text from a groundbreaking researcher, focusing on clarity and motivation to build intuition and understanding.

An innovative and groundbreaking text explaining how wireless AI can determine position, sense motion and vital signs, and identify events and people.

A groundbreaking introduction to vectors, matrices, and least squares for engineering applications, offering a wealth of practical examples.

Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.

This is a practical, rigorous guide to the compressive imaging revolution that has fundamentally changed modern image reconstruction.

This beginning graduate textbook teaches data science and machine learning methods for modeling, prediction, and control of complex systems.

This beginning graduate textbook teaches data science and machine learning methods for modeling, prediction, and control of complex systems.

This study allows readers to get to grips with the conceptual tools and practical techniques for building robust machine learning in the face of adversaries.

This study allows readers to get to grips with the conceptual tools and practical techniques for building robust machine learning in the face of adversaries.

The theory, methods and applications of matrix analysis are presented here in a novel theoretical framework.

The theory, methods and applications of matrix analysis are presented here in a novel theoretical framework.

This book describes how human hearing works and how to build machines that analyze sounds in the same way that people do.

This book describes how human hearing works and how to build machines that analyze sounds in the same way that people do.

Take an exhilarating journey through the modern revolution in statistics with two of the ringleaders.

Take an exhilarating journey through the modern revolution in statistics with two of the ringleaders.

Sure to be influential, this book lays the foundations for the use of algebraic geometry in statistical learning theory.

This modern approach integrates classical and contemporary methods, fusing theory and practice and bridging the gap to statistical learning.

This modern approach integrates classical and contemporary methods, fusing theory and practice and bridging the gap to statistical learning.

This unified random matrix approach to large-dimensional machine learning covers applications from power detection to deep neural networks.

This Element analyses real-world trial courts data with state-of-the-art machine learning and causal inference methods.

This Element analyses real-world trial courts data with state-of-the-art machine learning and causal inference methods.

A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.

A how-to guide and scientific tutorial covering the universe of reinforcement learning and control theory for online decision making.

A mathematical introduction to deep learning, written by a group of leading experts in the field.

Now in paperback: the new classic on the theory of statistical inference in statistical models with an infinite-dimensional parameter space.

This volume develops an effective theory approach to understanding deep neural networks of practical relevance.

A concise text that presents and analyzes the fundamental techniques and methods in optimization that are useful in data science.

A concise text that presents and analyzes the fundamental techniques and methods in optimization that are useful in data science.

Explains methods behind machine learning systems to personalize predictions to individual users, from recommendation to dating and fashion.

A diverse selection of data science topics explored through a mathematical lens.

This book introduces different approaches to developing recommender systems that automate choice-making strategies to provide affordable, personal, and high-quality recommendations.

This book describes theoretical advances in the study of artificial neural networks.