Machine learning under a modern optimization lens
By: Bertsimas, Dimitris
Contributor(s): Dunn, Jack [Co-author]
Material type: 
TextPublisher: Massachusetts Dynamic Ideas LLC 2019Description: xviii, 589 p. Includes reference and indexISBN: 9781733788502Subject(s): Machine learning | Lens optimization | Matrix methods | Perspective analyticsDDC classification: 006.31 Summary: The book provides an original treatment of machine learning (ML) using convex, robust and mixed integer optimization that leads to solutions to central ML problems at large scale that can be found in seconds/minutes, can be certified to be optimal in minutes/hours, and outperform classical heuristic approaches in out-of-sample experiments.
https://lib.mit.edu/record/cat00916a/mit.002821190
| Item type | Current location | Item location | Collection | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|---|---|
| Books | Vikram Sarabhai Library General Stacks | Slot 103 (0 Floor, West Wing) | Non-fiction | 006.31 B3M2 (Browse shelf) | Checked out | 14/05/2021 | 200849 |
Table of Contents
1. The optimization lenses
I. Regression and extension
II. Optimal trees for classification and regression
III. Prescriptive analytics
IV. The power of optimization over randomization
V. Unsupervised methods
VI. Matrix methods
VII. Optimization via a machine learning lens
The book provides an original treatment of machine learning (ML) using convex, robust and mixed integer optimization that leads to solutions to central ML problems at large scale that can be found in seconds/minutes, can be certified to be optimal in minutes/hours, and outperform classical heuristic approaches in out-of-sample experiments.
https://lib.mit.edu/record/cat00916a/mit.002821190
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