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.