Inference and Learning from Data: Volume 3; Learning (Hardback) 9781009218283

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Inference and Learning from Data: Volume 3 Learning Discover data-driven learning methods with the third volume of this extraordinary three-volume set. Ali H. Sayed (Author) 978100 9218283, Cambridge University Press Hardback, published 22 December 2022 990 pages25.5 x 18 x 4 cm, 1.76 kg 'The book series is timely and indispensable. It is a unique companion for graduate students and early-career researchers. The three volumes provide an extraordinary breadth and depth of techniques and tools, and encapsulate the experience and expertise of a world-class expert in the field. The pedagogically crafted text is written lucidly, yet never compromises rigor. Theoretical concepts are enhanced with illustrative figures, well-thought problems, intuitive examples, datasets, and MATLAB codes that reinforce readers' learning.' Abdelhak Zoubir, TU Darmstadt 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. Preface Notation 50. Least-squares problems 51. Regularization 52. Nearest-neighbor rule 53. Self-organizing maps 54. Decision trees 55. Naive Bayes classifier 56. Linear discriminant analysis 57. Principal component analysis 58. Dictionary learning 59. Logistic regression 60. Perceptron 61. Support vector machines 62. Bagging and boosting 63. Kernel methods 64. Generalization theory 65. Feedforward neural networks 66. Deep belief networks 67. Convolutional networks 68. Generative networks 69. Recurrent networks 70. Explainable learning 71. Adversarial attacks 72. Meta learning Author index Subject index. Subject Areas: Signal processing [UYS], Pattern recognition [UYQP], Machine learning [UYQM], Communications engineering / telecommunications [TJK], Information theory [GPF] Show more