Inference and Learning from Data Discover core topics in inference and learning with this extraordinary three-volume set. Ali H. Sayed (Author) 9781009218108, Cambridge University Press Multiple-component retail product, published 22 December 2022 3370 pages25.5 x 18 x 12 cm, 5.42 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. 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. 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. Subject Areas: Signal processing [UYS], Pattern recognition [UYQP], Machine learning [UYQM], Communications engineering / telecommunications [TJK], Information theory [GPF]
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