Inference and Learning from Data

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference.The first volume, Foundations, establishes core topics in inference and learning, and prepares readers for studying their practical application.The second volume, Inference, introduces readers to cutting-edge techniques for inferring unknown variables and quantities.The final volume, Learning, provides a rigorous introduction to state-of-the-art learning methods.A consistent structure and pedagogy is employed throughout all three volumes to reinforce student understanding, with over 1280 end-of-chapter problems (including solutions for instructors), over 600 figures, over 470 solved examples, datasets and downloadable Matlab code.Unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, statistical analysis, data science and inference.