Zdravko I. Botev, PhD, is the pioneer of several modern statistical methodologies, including the diffusion kernel density estimator, the generalized splitting method for rare-event simulation, the bandwidth perturbation matching method, the regenerative rejection sampling method, and the klimax method for feature selection. His contributions to computational statistics and data science have been recognized with honours such as the Christopher Heyde Medal from the Australian Academy of Science and the Gavin Brown Prize from the Australian Mathematical Society.

Dirk P. Kroese, PhD, is an Emeritus Professor in Mathematics and Statistics at the University of Queensland. He is known for his significant contributions to the fields of applied probability, mathematical statistics, machine learning, and Monte Carlo methods. He has published over 140 articles and 7 books. He is a pioneer of the well-known Cross-Entropy (CE) method, which is being used around the world to help solve difficult estimation and optimization problems in science, engineering, and finance.

Thomas Taimre, PhD, is a Senior Lecturer of Mathematics and Statistics at The University of Queensland. His research interests range from applied probability and Monte Carlo methods to applied physics and the remarkably universal self-mixing effect in lasers. He has published over 100 articles, holds a patent, and is the coauthor of Handbook of Monte Carlo Methods (Wiley).

Preface Notation 1. Importing, Summarizing, and Visualizing Data 2. Statistical Learning 3. Monte Carlo Methods 4. Unsupervised Learning 5. Regression 6. Feature Selection and Shrinkage 7. Reproducing Kernel Methods 8. Classification 9. Decision Trees and Ensemble Methods 10. Deep Learning 11. Reinforcement Learning Appendix A. Linear Algebra Appendix B. Functional Analysis Appendix C. Multivariate Differentiation and Optimization Appendix D. Probability and Statistics Appendix E. Python Primer Bibliography Index