Machine learning under a modern optimization lens
Material type: BookPublication details: 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.002821190Item type | Current library | Item location | Collection | Call number | Status | Date due | Barcode |
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Books | Vikram Sarabhai Library General Stacks | Rack 4-A / Slot 106 (0 Floor, West Wing) | Non-fiction | 006.31 B3M2 (Browse shelf(Opens below)) | Checked out | 14/08/2024 | 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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