Machine Learning Under a Modern Optimization Lens

Machine Learning.jpeg
Machine Learning.jpeg

Machine Learning Under a Modern Optimization Lens

94.99

by Dimitris Bertsimas and Jack Dunn

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.

 

Quantity:
Add to Cart

Structure of the book:

  • Part I covers robust, sparse, nonlinear, holistic regression andextensions.

  • Part II contains optimal classification and regression trees.

  • Part III outlines prescriptive MLmethods.

  • 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 inversecovariance 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 realworld.

  • Practical tractability not polynomial solvability leads to real world impact.

  • NP-hardness is an opportunity not anobstacle.

  • 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.