Topic: Higher Education

The cultural ramifications of online live streaming, including its effects on identity and power in digital spaces. Some consider live streaming—the broadcasting of video and/or audio footage live online—simply an...

Popular with and respected by instructors and students interested in a modeling approach, graphing, or graphing calculators, this book incorporates the benefits of technology and the philosophy of the reform...

This book provides the tools, the methods, and the theory to meet the challenges of contemporary data science applied to geographic problems and data. In the new world of pervasive,...

A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine...

An introduction to the intellectual foundations and practical utility of the recent work on fairness and machine learning. Fairness and Machine Learning introduces advanced undergraduate and graduate students to the intellectual...

Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective.What distinguishes reinforcement learning from...

The first comprehensive guide to distributional reinforcement learning, providing a new mathematical formalism for thinking about decisions from a probabilistic perspective. Distributional reinforcement learning is a new mathematical formalism for...

The mathematical theory of machine learning not only explains the current algorithms but can also motivate principled approaches for the future. This self-contained textbook introduces students and researchers of AI...

This textbook establishes a theoretical framework for understanding deep learning models of practical relevance. With an approach that borrows from theoretical physics, Roberts and Yaida provide clear and pedagogical explanations...

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses,...

Introductory Statistics, 2nd Edition provides an engaging, practical, and thorough overview of the core concepts and skills taught in most one-semester statistics courses. The text focuses on diverse applications from a...

A unique exploration of teleonomy—also known as “evolved purposiveness”—as a major influence in evolution by a broad range of specialists in biology and the philosophy of science. The evolved purposiveness...