Literature

The course material covers all exam-relevant topics in a quite self-contained manner. For more in-depth study, we recommend the following literature.

Helpful References for Prerequisites

• M. Deisenroth, A. Faisal, C. Ong. Mathematics for Machine Learning. URL
• H. Wickham, G. Grolemund. R for Data Science. URL
• Introductory R course on datacamp.com URL

Mathematical & Statistical Theory

• G. Strang. Linear Algebra and Learning from Data. Cambridge University Press, 2019. Serious course on matrices and applied linear algebra.
• K. B. Petersen, M. S. Pedersen, The Matrix Cookbook URL
• J. Watt, R. Borhani, A. Katsaggelos. Machine Learning Refined. Cambridge University Press, 2020. URL Check chapters 2-4 plus Appendix for insightful explanations and visualizations of a variety of optimization concepts.
• T. M. Cover, J. A. Thomas. Elements of Information Theory. Wiley, 2006. URL Good intro to information theory in first hundred pages, though lacking cross-connections to ML / statistics.
• https://www.3blue1brown.com/topics/linear-algebra
• https://www.3blue1brown.com/topics/calculus

Optimization

• D. Bertsimas, J. Dunn. Machine Learning Under a Modern Optimization Lens. Dynamic Ideas LLC, 2019.
• S. Sra, S. Nowozin, S. J. Wright. Optimization for Machine Learning. MIT Press, 2011.
• D. P. Bertsekas, Nonlinear Programming, Athena Scientific, 2016

R Programming

• N. Matloff. The Art of R Programming. No Starch Press, 2011. URL

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