عنوان

Inference and learning from data

(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)

Number

5774

.Language of Text, Soundtrack etc

انگلیسی

Title Proper

Inference and learning from data

General Material Designation

[electronic resources]

First Statement of Responsibility

Ali H. Sayed.

Statement of Responsibility Relating to Edition

978-1-009-21810-8 (3 Volume Set: Hbk)

Name of Publisher, Distributor, etc.

978-1-009-21812-2 (Volume I: Hbk)

Other Physical Details

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

Series Title

5774

Text of Note

Includes bibliographical references and indexes.

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

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.

Big data.

Relator Code

Author

Sayed, Ali H.,

Country

ایران

Agency

Central Library of the Kurdistan University

e

BL

280328

a

Y