Books like Artificial intelligence and statistics by William A. Gale

The Elements of Statistical Learning explains key statistical methods that underlie modern machine learning, data mining, and bioinformatics, covering supervised and unsupervised learning techniques such as classification trees, neural networks, and support vector machines.

Deep Learning is a comprehensive textbook that equips students and practitioners with the foundational knowledge and practical skills needed to enter the fields of machine learning and deep learning.

A practical guide to Bayesian statistical analysis that prioritizes computation, model checking, and data‑collection design, with examples spanning regression, hierarchical, robust, generalized linear, and mixture models.

Applied Linear Statistical Models – a comprehensive textbook by John Neter that covers linear models, regression analysis, analysis of variance, and experimental design for statisticians and researchers.

A textbook for statistics that incorporates humor to make studying more engaging.

A practical guide for applying statistical methods to business and economics, offering clear development of techniques illustrated with real‑world applications and data‑driven insights.

A concise textbook that teaches statistical reasoning tailored for research in the behavioral sciences, covering foundational concepts, inference, and design methods and techniques for analyzing behavioral data.

An Introduction to Statistical Learning offers a practitioner‑friendly guide to modern statistical learning methods, including linear regression, classification, resampling, shrinkage, tree‑based models, SVMs, and clustering, with R tutorials and real‑world examples.

Applied Statistics is a comprehensive textbook that teaches statistical methods for economics, management, and social science applications, featuring exercises and problem sets for practical learning.

"Data analysis is changing fast. Driven by a vast range of application domains and affordable tools, machine learning has become mainstream. Unsupervised data analysis, including cluster analysis, factor analysis, and low dimensionality mapping methods continually being updated, have reached new heights of achievement in the incredibly rich data world that we inhabit.Statistical Learning and Data Science is a work of reference in the rapidly evolving context of converging methodologies. It gathers contributions from some of the foundational thinkers in the different fields of data analysis to the major theoretical results in the domain. On the methodological front, the volume includes conformal prediction and frameworks for assessing confidence in outputs, together with attendant risk. It illustrates a wide range of applications, including semantics, credit risk, energy production, genomics, and ecology. The book also addresses issues of origin and evolutions in the unsupervised data analysis arena, and presents some approaches for time series, symbolic data, and functional data.Over the history of multidimensional data analysis, more and more complex data have become available for processing. Supervised machine learning, semi-supervised analysis approaches, and unsupervised data analysis, provide great capability for addressing the digital data deluge. Exploring the foundations and recent breakthroughs in the field, Statistical Learning and Data Science demonstrates how data analysis can improve personal and collective health and the well-being of our social, business, and physical environments. "--