Textbook in PDF format

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 techniques such as least-squares methods, regularization, online learning, kernel methods, feedforward and recurrent neural networks, meta-learning, and adversarial attacks. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including complete 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, data and inference.
Key features
- Unique in its scale and depth, this is a comprehensive introduction to methods in data-driven learning and inference
- Over 1300 end-of-chapter problems (with solutions for instructors), 600 figures and 470 in-text solved examples across the three volumes
- A phenomenal contribution by a world authority in the field
- Covers sufficient topics across the volumes for the construction of a variety of courses covering a wide range of themes