Machine Learning Under a Moder

Dimitris Bertsimas

ISBN 10: 1733788506 / ISBN 13: 9781733788502

Published by Dynamic Ideas LLC

About this Item

Item in very good condition! Textbooks may not include supplemental items i.e. CDs, access codes etc.

Seller Inventory # 00089187268

Bibliographic Details

• Title: Machine Learning Under a Moder
• Publisher: Dynamic Ideas LLC
• Language: English
• ISBN 10: 1733788506
• ISBN 13: 9781733788502
• Binding: Hardcover
• Condition: Very Good

About this title

Synopsis

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.

Structure of the book:

Part I covers robust, sparse, nonlinear, holistic regression and extensions.

Part II contains optimal classification and regression trees.

Part III outlines prescriptive ML methods.

Part IV shows the power of optimization over randomization in design of experiments, exceptional responders, stable regression and the bootstrap.

Part V describes unsupervised methods in ML: optimal missing data imputation and interpretable clustering.

Part VI develops matrix ML methods: sparse PCA, sparse inverse covariance estimation, factor analysis, matrix and tensor completion
.

Part VII demonstrates how ML leads to interpretable optimization.

Philosophical principles of the book:
Interpretability is materially important in the real world.
Practical tractability not polynomial solvability leads to real world impact.

NP-hardness is an opportunity not an obstacle.

ML is inherently linked to optimization not probability theory.
Data represent an objective reality; models only exist in our imagination.

Optimization has a significant edge over randomization
.

The ultimate objective in the real world is prescription, not prediction.