Literature and readings
Completion requirements
The main literature for machine learning:
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:
- Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning. Springer, Berlin. (freely available)
- Yuxi Li (2018). Deep reinforcement learning, https://arxiv.org/abs/1810.06339
- Yoav Shoham, Kevin Leyton-Brown: Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations. Cambridge University Press, 200, freely available
- Jurafsky, Daniel and James, Martin (2019): Speech and
Language Processing, 3rd edition in progres, freely available
Richard S. Sutton and Andrew G. Barto: Reinforcement Learning, An Introduction, 2nd edition, MIT press, 2018, freely available
Kononenko, I., Robnik-Šikonja, M.: Inteligentni sistemi. Založba FE in FRI, 2010 (in Slovene, mostly outdated, available in the bookshop at the entrance)