Machine learning under a modern optimization lens

By: Bertsimas, Dimitris
Contributor(s): Dunn, Jack [Co-author]
Material type: TextTextPublisher: 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
Tags from this library: No tags from this library for this title. Log in to add tags.
    Average rating: 0.0 (0 votes)
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

There are no comments for this item.

to post a comment.

Powered by Koha