A Additional Reading

A.1 Books

• An Introduction to Statistical Learning
  • Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani
• The Elements of Statistical Learning
  • Trevor Hastie, Robert Tibshirani, and Jerome Friedman
• Mathematics for Machine Learning
  • Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong
• Understanding Machine Learning: From Theory to Algorithms
  • Shai Shalev-Shwartz and Shai Ben-David
• Foundations of Machine Learning
  • Mehryar Mohri, Afshin Rostamizadeh, and Ameet Talwalkar
• The caret Package
  • Max Kuhn
• Feature Engineering and Selection: A Practical Approach for Predictive Models
  • Max Kuhn and Kjell Johnson
• Applied Predictive Modeling
  • Max Kuhn and Kjell Johnson
• Machine Learning: A Probabilistic Perspective
  • Kevin Murphy
• Probability for Statistics and Machine Learning
  • Anirban DasGupta
• From Linear Models to Machine Learning
  • Norman Matloff
• Interpretable Machine Learning
  • Christoph Molnar
• Grokking Deep Learning
  • Andrew W. Trask

A.2 Papers

• Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?
  • Manuel Fernandez-Delgado, Eva Cernadas, Senen Barro, Dinani Amorim
• Statistical Modeling: The Two Cultures
  • Leo Breiman
• 50 Years of Data Science
  • David Donoho

A.3 Blog Posts

• Peekaboo: Don’t cite the No Free Lunch Theorem
• aleatoric: Overfitting: A Guided Tour
• Math for Machines: Optimal Boundaries
• Variance Explained: What’s the difference between data science, machine learning, and artificial intelligence?

A.4 Miscellaneous

• Machine Learning verus Statistics, A Glossary
  • Rob Tibshirani