Recommended Literature

Literature

Held, L, Sabanés Bové, D.: Likelihood and Bayesian Inference. Springer 2020, Chapters 1-7.
Fahrmeir, L., Kneib, Th., Lang, S., Marx, B.D.: Regression. Springer 2021, Chapters 1-4.
Bischl, B. et al.: Introduction to Machine Learning (I2ML). https://slds-lmu.github.io/i2ml/ The first 10 chapters of I2ML cover the BSc part.
Anton, H., & Rorres, C. (2013). Elementary linear algebra: applications version. John Wiley & Sons.
Peter Philip (2024). Calculus I and Calculus II for Statistics students. Lecture Notes, LMU Munich.
Grimmett, G. and Stirzaker, D.: Probability and Random Processes. Oxford University Press, 2001, Chapters 1-4 and 7

In-depth Literature

G. James, D. Witten, T. Hastie, R. Tibshirani. An Introduction to Statistical Learning. MIT Press, 2010.
The elements of statistical learning: data mining, inference and prediction. T. Hastie, R. Tibshirani, and J. Friedman. Springer, 2 edition, (2009)
E. Alpaydin. Introduction to Machine Learning. MIT Press, 2010.
Bishop, C. M. (2007). Pattern Recognition and Machine Learning (Information Science and Statistics). Springer.
K. Murphy. Machine Learning: a Probabilistic Perspective link
Grimmett, G., Stirzaker D.: Probability and Random Processes. Oxford University Press, 2020, Chapters 1-4 and 7.1-7.4
Lang, S.: Matrixalgebra. https://www.uibk.ac.at/statistics/personal/lang/publications/matrixalgebra.pdf, Chapters 1–9
Hoffman, K. M., Kunze, R. (1971). Linear Algebra. Englewood Cliffs, NJ: Prentice-Hall.
Casella, G., Berger, R. L. (2002). Statistical inference. Duxbury Pacific Grove, CA.
Wasserman, L. (2010). All of statistics: a concise course in statistical inference. New York: Springer.

Deisenroth, M.P., Faisal, A.A., Ong, C.S.: Mathematics for Machine Learning. https://mml-book.github.io/book/mml-book.pdf, 2020, Chapters 1-6

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