**Course Schedule**

**Reading List**

Notes covering the classes will be provided in the form of independent chapters of a book currently in draft format. Additional information will be given through the slides associated with classes (where applicable). The books/papers listed below are useful general reference reading, especially from the theoretical viewpoint. A list of additional suggested readings will also be provided separately for each class.

**Book (draft)**

- L. Rosasco and T. Poggio,
**Machine Learning: a Regularization Approach, MIT-9.520 Lectures Notes**, Manuscript, Dec. 2017**(provided)**.

**Primary References**

- S. Shalev-Shwartz and S. Ben-David.
**Understanding Machine Learning: From Theory to Algorithms.**Cambridge University Press, 2014. (MIT Library Link) - O. Bousquet, S. Boucheron and G. Lugosi.
**Introduction to Statistical Learning Theory**. Advanced Lectures on Machine Learning, LNCS 3176, pp. 169-207. (Eds.) Bousquet, O., U. von Luxburg and G. Ratsch, Springer, 2004. (MIT Library Link) - F. Cucker and S. Smale.
**On The Mathematical Foundations of Learning**. Bulletin of the American Mathematical Society, 2002. (MIT Library Link) - L. Devroye, L. Gyorfi, and G. Lugosi.
**A Probabilistic Theory of Pattern Recognition.**Springer, 1997. (MIT Library Link) - T. Evgeniou, M. Pontil and T. Poggio.
**Regularization Networks and Support Vector Machines.**Advances in Computational Mathematics, 2000. (MIT Library Link) - T. Poggio and S. Smale.
**The Mathematics of Learning: Dealing with Data.**Notices of the AMS, 2003. (MIT Library Link) - V. N. Vapnik.
**Statistical Learning Theory.**Wiley, 1998. (MIT Library Link)

**Papers of Interest**

- T. Poggio, H. Mhaskar, L. Rosasco, B. Miranda, and Q. Liao.
**Why and When can Deep-but not Shallow-Networks Avoid the Curse of Dimensionality: A Review**. International Journal of Automation and Computing, 1-17, 2017. - T. Poggio, M. Fraser Compositional sparsity of learnable functions, Bulleting of the American Mathematical Society, 2024.
- M. Xu, A. Rangamani, Q. Liao, T. Galanti, T. Poggio .
__Dynamics in Deep Classifiers Trained with the Square Loss: Normalization, Low Rank, Neural Collapse, and Generalization Bounds__, Research, vol 6, 2023. - Y. LeCun, Y. Bengio and G. Hinton,
__Deep Learning,__Nature, 436-444, 2015. - D. Silver et al,
__Mastering the game of Go with deep neural networks and tree search__. Nature, 484-489, 2016. - J. Jumper et al,
__Highly accurate protein structure prediction with AlphaFold__. Nature, 583-589. 2021. - A. Vaswani et al,
__Attention is all you need.__Advances in neural information processing systems, 30. 2017.

**Resources and links**

- Machine Learning 2017-2018. University of Genoa, graduate ML course.
- L. Rosasco,
**Introductory Machine Learning Notes**, University of Genoa, ML 2016/2017 lectures notes, Oct. 2016.