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عنوان
Inference and learning from data

پدید آورنده
Ali H. Sayed.

موضوع
Big data.

رده

کتابخانه
Central library and document university of Kurdistan

محل استقرار
استان: Kurdistan ـ شهر: Sanandaj

Central library and document university of Kurdistan

تماس با کتابخانه : 9-08733624006و 08733664600

INTERNATIONAL STANDARD BOOK NUMBER

(Number (ISBN
978-1-009-21810-8 (3 Volume Set: Hbk)
(Number (ISBN
978-1-009-21812-2 (Volume I: Hbk)
(Number (ISBN
978-1-009-21826-9 (Volume II: Hbk)
(Number (ISBN
78-1-009-21828-3 (Volume III: Hbk)

NATIONAL BIBLIOGRAPHY NUMBER

Number
5774

LANGUAGE OF THE ITEM

.Language of Text, Soundtrack etc
انگلیسی

TITLE AND STATEMENT OF RESPONSIBILITY

Title Proper
Inference and learning from data
General Material Designation
[electronic resources]
First Statement of Responsibility
Ali H. Sayed.

EDITION STATEMENT

Statement of Responsibility Relating to Edition
978-1-009-21810-8 (3 Volume Set: Hbk)

.PUBLICATION, DISTRIBUTION, ETC

Name of Publisher, Distributor, etc.
978-1-009-21812-2 (Volume I: Hbk)

PHYSICAL DESCRIPTION

Other Physical Details
978-1-009-21826-9 (Volume II: Hbk)

SERIES

Series Title
5774

INTERNAL BIBLIOGRAPHIES/INDEXES NOTE

Text of Note
Includes bibliographical references and indexes.

CONTENTS NOTE

Text of Note
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.
0

SUMMARY OR ABSTRACT

Text of Note
This extraordinary three-volume work provides an accessible, comprehensive introduction to mathematical and statistical techniques for data-driven learning and inference. Ideal for early-career researchers and graduate students across signal processing, machine learning, statistics and data science.

TOPICAL NAME USED AS SUBJECT

Big data.

PERSONAL NAME - PRIMARY RESPONSIBILITY

Relator Code
Author
Sayed, Ali H.,

ORIGINATING SOURCE

Country
ایران
Agency
Central Library of the Kurdistan University

e

BL
280328

a
Y

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