Machine learning

Discover core topics in inference and learning with the first volume of this extraordinary three-volume set.

WINNER: Independent Press Awards 2023 - Category: Technology AI presents a new paradigm in software development, representing the biggest change to how we think about quality and testing in decades.

WINNER: Independent Press Awards 2023 - Category: Technology AI presents a new paradigm in software development, representing the biggest change to how we think about quality and testing in decades.

This introduction to the theory of variational Bayesian learning summarizes recent developments and suggests practical applications.

Introduces cutting-edge research on machine learning theory and practice, providing an accessible, modern algorithmic toolkit.

Introduces cutting-edge research on machine learning theory and practice, providing an accessible, modern algorithmic toolkit.

A thorough introduction to probabilistic numerics showing how to build more flexible, efficient, or customised algorithms for computation.

This book provides an in-depth discussion of challenges encountered in deploying real-life large-scale systems and the state-of-the-art solutions in personalization.

An authoritative and in-depth treatment of state space methods, with a range of applications in neural and clinical data.

An authoritative and in-depth treatment of state space methods, with a range of applications in neural and clinical data.

This book sheds new light on the form and function of morphemes in construction of words in the Bengali language.

Top researchers investigate the main challenges and state of the art in automated negotiation and discuss potential applications.

Top researchers investigate the main challenges and state of the art in automated negotiation and discuss potential applications.

Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

A coherent source for applications including robotics, computational biology, graphics, manufacturing, aerospace applications and medicine.

Covering the fundamentals of kernel-based learning theory, this is an essential resource for graduate students and professionals in computer science.

Covers text mining and link detection algorithms, pre-processing techniques, knowledge representation, visualization and applications.

Covering the fundamentals of kernel-based learning theory, this is an essential resource for graduate students and professionals in computer science.

This is a comprehensive introduction to Support Vector Machines, a generation learning system based on advances in statistical learning theory.

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

A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.

This is a comprehensive introduction to Support Vector Machines, a generation learning system based on advances in statistical learning theory.

This integrated collection covers a range of parallelization platforms, concurrent programming frameworks and machine learning settings, with case studies.

A comprehensive and consistent theory of estimation, including a description of a powerful new tool, the generalized maximum capacity estimator.

Introductory textbook presenting relational methods in machine learning.

This book introduces theories, methods and applications of density ratio estimation, a newly emerging paradigm in the machine learning community.

A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.

The first unified treatment of time series modelling techniques spanning machine learning, statistics, engineering and computer science.

This state-of-the-art overview of the field describes how phase transitions occur and teaches appropriate methods for tackling the consequent problems.

Gives a solid basis for conducting performance evaluations of learning algorithms in practical settings with an emphasis on classification algorithms.

This second edition explains how computer software is designed to perform the tasks required for sophisticated statistical analysis.

Discover connections between these transformative and impactful technologies, through comprehensive introductions and real-world examples.

Introduces fundamental theory, basic and advanced algorithms, and system design issues.

Rigorous treatment of the theory of deep learning from first principles, with applications to beautiful problems in the natural sciences.

A comprehensive text on foundations and techniques of graph neural networks with applications in NLP, data mining, vision and healthcare.

A coherent introduction to core concepts and deep learning techniques that are critical to academic research and real-world applications.

Now in paperback and fortified with exercises, this brilliant, enjoyable text demystifies data science, statistics and machine learning.

This cutting-edge volume offers a theoretical and applied introduction to the emerging legal technology and informatics industry.

Presents carefully selected supervised and unsupervised learning methods from basic to state-of-the-art,in a coherent statistical framework.

Presents carefully selected supervised and unsupervised learning methods from basic to state-of-the-art,in a coherent statistical framework.

This element introduces machine learning (ML) tools that can help asset managers discover economic and financial theories.

This element introduces machine learning (ML) tools that can help asset managers discover economic and financial theories.

Modern introduction to machine learning with neural networks.

Probabilistic Programs.

Connects fundamental mathematical theory with real-world problems, through efficient and scalable optimization algorithms.

This accessible course on a central player in modern statistical practice connects models with methodology, without need for advanced math.

This concise course in data analysis and inference for the mathematically literate builds on survey sampling and designed experiments.

Introduces exciting new methods for assessing algorithms for problems ranging from clustering to linear programming to neural networks.

Introduces exciting new methods for assessing algorithms for problems ranging from clustering to linear programming to neural networks.

Explores the critical role time plays in our understanding of causality, across psychology, biology, physics and the social sciences.