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.