Ali H. Sayed (Ecole Polytechnique Federale de Lausanne)

Inference and Learning from Data: Volume 2

Inference

• Gebundenes Buch

Produktbeschreibung

Written in an engaging and rigorous style by a world authority in the field, this is an accessible and comprehensive introduction to techniques for inferring unknown variables and quantities. With downloadable Matlab code and solutions for instructors, this is the ideal introduction for students of data science, machine learning and engineering.

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Produktdetails

• Verlag: Cambridge University Press
• Seitenzahl: 1070
• Erscheinungstermin: 22. Dezember 2022
• Englisch
• Abmessung: 254mm x 183mm x 47mm
• Gewicht: 1860g
• ISBN-13: 9781009218269
• ISBN-10: 1009218263
• Artikelnr.: 64671893

Autorenporträt

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 Chairman of Electrical Engineering at the University of California, Los Angeles, 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 the 2022 IEEE Fourier Award and the 2020 IEEE Norbert Wiener Society Award. He is a Fellow of the IEEE.

Inhaltsangabe

Preface
Notation
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.