Machine Learning Under a Modern Optimization Lens

Brief

Book

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. 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.