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. Note that some of the books are rather detailed and involved, and more geared towards a larger lecture in a Master’s degree.

We recommend to buy and read at least one standard reference on ML, for BSc level this might be the James, for the MSc level the Hastie, Bishop, Murphy or Alplaydin, the Shalev-Shwartz for a mathematical entry point.

Helpful References for Prerequisites

If you need to read up on some of the required topics (see Prerequisites), this list might help. We tried to keep it as short as possible.

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

Machine Learning

• K. Kersting, C. Lampert, C. Rothkopf. Wie Maschinen Lernen. Springer, 2019. URL German, informal, intuitive introduction to ML. Lower than BSc level, maybe more targeted at pupils or a non-academic audience. Read if you want a very light-weight introduction into the field, or buy as present for relatives and friends if they ask what you are doing.
• G. James, D. Witten, T. Hastie, R. Tibshirani. An Introduction to Statistical Learning. MIT Press, 2010. URL Beginner-level introduction with applications in R. Very well suited for the BSc level.
• T. Hastie, R. Tibshirani, J. Friedman. The Elements of Statistical Learning. Springer, 2009. URL Standard reference for statistics-flavored ML.
• C. M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006. URL Standard reference for ML-flavored ML.
• S. Shalev-Shwartz, S. Ben-David. Understanding machine learning: From Theory to Algorithms. Cambridge University Press, 2014. URL Great, thorough introduction to ML theory. Math-style book with definitions and proofs.
• E. Alpaydin. Introduction to Machine Learning. MIT Press, 2010. URL Standard reference with broad coverage; easy to read.
• K. Murphy. Machine Learning: a Probabilistic Perspective. MIT Press, 2012. URL Standard reference; quite extensive; statistical/probabilistic lens.
• F. Provost, T. Fawcett. Data Science for Business. O’Reilly, 2013. URL A very good, applied and easy-to-read book by 2 well-known ML scientists. Contains many practical aspects that are missing in other references. Probably a good idea to read this in any case.
• N. Japkowicz. Evaluating Learning Algorithms (A Classification Perspective). Cambridge University Press, 2011. Nice reading on performance measures, resampling methods and (some) statistical tests for benchmarking in ML; only for classification.
• B. Bischl et al. Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges. arXiv preprint 2021. URL Our tutorial paper on HPO.
• I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. MIT Press, 2016. URL Standard, modern reference for DL.
• J. Friedman, T. Hastie, R. Tibshirani. Additive logistic regression: a statistical view of boosting (With discussion and a rejoinder by the authors). Ann. Statist. 2000. URL

Mathematical & Statistical Theory

• G. Strang. Linear Algebra and Learning from Data. Cambridge University Press, 2019. Serious course on matrices and applied linear algebra.
• S. Axler. Linear Algebra Done Right. Springer, 2015. URL Linear Algebra from a more theoretical but still beginner-friendly perspective
• A. M. Mood, F. A. Graybill, D. C. Boes. Introduction to the Theory of Statistics, McGraw-Hill 1974. URL Beginner-friendly intro to statistics; bit on the mathy side.
• 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.

R Programming

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

We use the mlr3 package for machine learning in R quite heavily.

• Central project page and learning resources: https://mlr3.mlr-org.com/, in particular
  • the book,
  • the gallery, and
  • the cheatsheets.
• GitHub page: https://github.com/mlr-org/mlr3