Literature and readings
Completion requirements
The main literature for machine learning:
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James, G., Witten, D., Hastie, T., Tibshirani, R. and Taylor, J., 2023. An Introduction to Statistical Learning: With Applications in Python. New York: Springer. Freely available at https://www.statlearning.com/ (the same book exists for R)
Further readings:
- Kevin P. Murphy: Probabilistic Machine Learning: An Introduction. MIT Press, 2022
- Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning. Springer, Berlin. (freely available)
- Jurafsky, Daniel and James, Martin (2024): Speech and Language Processing, 3rd edition in progress, freely available
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Richard S. Sutton and Andrew G. Barto: Reinforcement Learning, An Introduction, 2nd edition, MIT Press, 2018, freely available
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Kevin P. Murphy: Probabilistic Machine Learning: Advanced Topics. MIT Press, 2023