Inference and Learning from Data, vol. 3: Learning

A. H. Sayed, Inference and Learning from Data, Volume 3: Learning, Cambridge University Press, 2022, li+1029pp.


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. This final volume, Learning, builds on the foundational topics established in volume I to provide a thorough introduction to learning methods, addressing technique such as least-squares methods, regularization, online learning, kernel methods, generalization theory, feedforward, convolutional, recurrent, and generative neural networks, meta learning, explainable learning, and adversarial attacks. A consistent structure and pedagogy are employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including solutions for instructors), 280 figures, 100 solved examples, datasets, and downloadable MATLAB code. Supported by sister volumes Foundations and Inference, and 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.

About the Author

Ali H. Sayed is Professor and Dean of Engineering at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He has also served as Distinguished Professor and Chair of Electrical Engineering at the University of California, Los Angeles (UCLA), USA, and as President of the IEEE Signal Processing Society. He is a member of the US National Academy of Engineering (NAE) and The World Academy of Sciences (TWAS), and a recipient of several awards, including the 2022 IEEE Fourier Technical Field Award and the 2020 IEEE Norbert Wiener Society Award. He is a Fellow of the IEEE, EURASIP, and AAAS.

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