Inference and Learning from Data
- Author: Ali H. Sayed, École Polytechnique Fédérale de Lausanne
- Date Published: December 2022
- availability: Available
- format: Multiple copy pack
- isbn: 9781009218108
Multiple copy pack
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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.
Read more- Unique in its scale and depth, this is a comprehensive introduction to methods in data-driven learning and inference
- Over 1280 end-of-chapter problems (with complete solutions for instructors), 600 figures and 470 in-text solved examples
- 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
Reviews & endorsements
'Inference and Learning from Data is a uniquely comprehensive introduction to the signal processing foundations of modern data science. Lucidly written, with a carefully balanced choice of topics, this textbook is an indispensable resource for both graduate students and data science practitioners, a piece of lasting value.' Helmut Bölcskei, ETH Zurich
See more reviews'This textbook provides a lucid and magisterial treatment of methods for inference and learning from data, aided by hundreds of solved examples, computer simulations, and over 1000 problems. The material ranges from fundamentals to recent advances in statistical learning theory; variational inference; neural, convolutional, and Bayesian networks; and several other topics. It is aimed at students and practitioners, and can be used for several different introductory and advanced courses.' Thomas Kailath, Stanford University
'A tour de force comprehensive three-volume set for the fast-developing areas of data science, machine learning, and statistical signal processing. With masterful clarity and depth, Sayed covers, connects, and integrates background fundamentals and classical and emerging methods in inference and learning. The books are rich in worked-out examples, exercises, and links to data sets. Commentaries with historical background and contexts for the topics covered in each chapter are a special feature.' Mostafa Kaveh, University of Minnesota
'This is the first of a three-volume series covering from fundamentals to the many various methods in inference and learning from data. Professor Sayed is a prolific author of award-winning books and research papers who has himself contributed significantly to many of the topics included in the series. With his encyclopedic knowledge, his careful attention to detail, and in a very approachable style, this first volume covers the basics of matrix theory, probability and stochastic processes, convex and non-convex optimization, gradient-descent, convergence analysis, and several other advanced topics that will be needed for volume II (Inference) and volume III (Learning). This series, and in particular this volume, will be a must-have for educators, students, researchers, and technologists alike who are pursuing a systematic study, want a quick refresh, or may use it as a helpful reference to learn about these fundamentals.' Jose Moura, Carnegie Mellon University
'Volume I of Inference and Learning from Data provides a foundational treatment of one of the most topical aspects of contemporary signal and information processing, written by one of the most talented expositors in the field. It is a valuable resource both as a textbook for students wishing to enter the field and as a reference work for practicing engineers.' Vincent Poor, Princeton University
'Inference and Learning from Data, Vol. I: Foundations offers an insightful and well-integrated primer with just the right balance of everything that new graduate students need to put their research on a solid footing. It covers foundations in a modern way - emphasizing the most useful concepts, including proofs, and timely topics which are often missing from introductory graduate texts. All in one beautifully written textbook. An impressive feat! I highly recommend it.' Nikolaos Sidiropoulos, University of Virginia
'This exceptional encyclopedic work on learning from data will be the bible of the field for many years to come. Totaling more than 3000 pages, this three-volume book covers in an exhaustive and timely manner the topic of data science, which has become critically important to many areas and lies at the basis of modern signal processing, machine learning, artificial intelligence, and their numerous applications. Written by an authority in the field, the book is really unique in scale and breadth, and it will be an invaluable source of information for students, researchers, and practitioners alike.' Peter Stoica, Uppsala University
'Very meticulous, thorough, and timely. This volume is largely focused on optimization, which is so important in the modern-day world of data science, signal processing, and machine learning. The book is classical and modern at the same time - many classical topics are nicely linked to modern topics of current interest. All the necessary mathematical background is covered. Professor Sayed is one of the foremost researchers and educators in the field and the writing style is unhurried and clear with many examples, truly reflecting the towering scholar that he is. This volume is so complete that it can be used for self-study, as a classroom text, and as a timeless research reference.' P. P. Vaidyanathan, Caltech
'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
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×Product details
- Date Published: December 2022
- format: Multiple copy pack
- isbn: 9781009218108
- length: 3370 pages
- dimensions: 255 x 180 x 120 mm
- weight: 5.42kg
- availability: Available
Table of Contents
Volume I. Foundations:
1. Matrix theory
2. Vector differentiation
3. Random variables
4. Gaussian distribution
5. Exponential distributions
6. Entropy and divergence
7. Random processes
8. Convex functions
9. Convex optimization
10. Lipschitz conditions
11. Proximal operator
12. Gradient descent method
13. Conjugate gradient method
14. Subgradient method
15. Proximal and mirror descent methods
16. Stochastic optimization
17. Adaptive gradient methods
18. Gradient noise
19. Convergence analysis I: stochastic gradient algorithms
20. Convergence analysis II: stochasic subgradient algorithms
21. Convergence analysis III: stochastic proximal algorithms
22. Variance-reduced methods I: uniform sampling
23. Variance-reduced methods II: random reshuffling
24. Nonconvex optimization
25. Decentralized optimization I: primal methods
26. Decentralized optimization II: primal-dual methods
Author index
Subject index. Volume II. Inference:
27. Mean-Square-Error inference
28. Bayesian inference
29. Linear regression
30. Kalman filter
31. Maximum likelihood
32. Expectation maximization
33. Predictive modeling
34. Expectation propagation
35. Particle filters
36. Variational inference
37. Latent Dirichlet allocation
38. Hidden Markov models
39. Decoding HMMs
40. Independent component analysis
41. Bayesian networks
42. Inference over graphs
43. Undirected graphs
44. Markov decision processes
45. Value and policy iterations
46. Temporal difference learning
47. Q-learning
48. Value function approximation
49. Policy gradient methods
Author index
Subject index. Volume III. Learning:
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. Feed forward 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.
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