Machine learning under a modern optimization lens

By: Bertsimas, DimitrisContributor(s): Dunn, Jack [Co-author]Material type: BookBookPublication 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.002821190
List(s) this item appears in: Machine Learning
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Rack 4-A / Slot 106 (0 Floor, West Wing) Non-fiction 006.31 B3M2 (Browse shelf(Opens below)) Checked out 18/04/2024 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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