Probabilistic machine learning /
Material type: TextSeries: Adaptive computation and machine learning seriesPublisher: Cambridge, England : The MIT Press, [2022 - 2023]Copyright date: ©2022 - 2023Description: 2 volumes (various paging) : illustrations ; 24 cmContent type:- text
- unmediated
- volume
- 9780262046824
- 9780262048439
- 006.31 MUP 23
Item type | Current library | Collection | Call number | Vol info | Status | Date due | Barcode | Item holds | |
---|---|---|---|---|---|---|---|---|---|
Book Non-borrowing | Library D | Information Technology | 006.31 MUP (Browse shelf(Opens below)) | Advanced Topics | Not For Loan | 1004551 | |||
Book | Library D | Information Technology | 006.31 MUP (Browse shelf(Opens below)) | Advanced Topics | Available | 1004552 | |||
Book Non-borrowing | Library D | Information Technology | 006.31 MUP (Browse shelf(Opens below)) | An Introduction | Not For Loan | 1004549 | |||
Book | Library D | Information Technology | 006.31 MUP (Browse shelf(Opens below)) | An Introduction | Available | 1004550 |
Includes bibliographical references and index.
An Introduction: I Introduction -- II Linear Models -- III Deep Neural Networks -- IV Nonparametric Models -- V Beyond Supervised Learning.
Advanced Topics: I Introduction -- II Inference -- III Prediction -- IV Generation -- V Discovery -- VI Action.
"This book provides a detailed and up-to-date coverage of machine learning. It is unique in that it unifies approaches based on deep learning with approaches based on probabilistic modeling and inference. It provides mathematical background (e.g. linear algebra, optimization), basic topics (e.g., linear and logistic regression, deep neural networks), as well as more advanced topics (e.g., Gaussian processes). It provides a perfect introduction for people who want to understand cutting edge work in top machine learning conferences such as NeurIPS, ICML and ICLR"-- Provided by publisher.
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