Generale
- McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (1955) A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence. Reperibile qui: http://jmc.stanford.edu/articles/dartmouth/dartmouth.pdf
Macro-area: Reti neurali
- David Kriesel, 2007, A Brief Introduction to Neural Networks, Capitoli 1;2;3;4;5;8 Reperibile in http://www.dkriesel.com/en/science/neural_networks
• Ian Goodfellow and YoshuaBengio and Aaron Courville, Deep Learning, An MIT Press book
https://www.deeplearningbook.org/
Part I, Chapters 2–5: Applied Math and Machine Learning Basics (includes linear algebra, probability and information theory, numerical computation, and machine learning basics)
And Part II, Chapters 6–8: Modern Practical Deep Networks (deep feedforward networks and regularization for deep learning, optimization for training deep learning).
• A. NG, Machine Learning Yearning
https://freecomputerbooks.com/Machine-Learning-Yearning.html#downloadLinks
Macro-area: Reinforcement Learning
- Sutton & Barto: Reinforcement Learning: An Introduction (Second Edition). Capitoli 1; 2; 3; 6 (solo 6.1;6.2;6.5); 14; 15. Reperibile on-line: http://www.incompleteideas.net/book/RLbook2020.pdf
- Schultz, W., Dayan, P., & Montague, P. R. (1997). A neural substrate of prediction and reward. Science, 275(5306), 1593-1599. Reperibile on-line
Macro-area: Neuroscienze computazionali
- Wulfram Gerstner, Werner M. Kistler, Richard Naud and Liam Paninski, 2014, Capitoli 1 e 2. Neuronal Dynamics online book. Reperiblile qui:
https://neuronaldynamics.epfl.ch/
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- Ulteriori materiale verrà fornito nell’ambito delle lezioni interattive.