Follow the link for each class to find a detailed description, suggested readings, and class slides. Some of the later classes may be subject to reordering or rescheduling.

Class	Date	Title	Instructor(s)
Class 01	Thu Sep 09	The Course at a Glance	TP
Class 02	Tue Sep 14	Statistical Learning Setting	LR
Class 03	Thu Sep 16	Regularized Least Squares	LR
Class 04	Tue Sep 21	Features and Kernels	LR
Class 05	Thu Sep 23	Logistic Regression and Support Vector Machines	LR
Class 06	Tue Sep 28	Learning with Stochastic Gradients	LR
Class 07	Thu Sep 30	Implicit Regularization with linear networks	LR
Class 08	Tue Oct 05	Learning with Random Features	LR
Class 09	Thu Oct 07	Error Decomposition and Approximation Error	LR
Monday 11th October – Indigenous People’s Day
Class 10	Tue Oct 12	Estimation Error and Generalization Gap	LR
Class 11	Thu Oct 14	Stability of Ridge and Ridgeless Regression	TP + AR
Class 12	Tue Oct 19	Introduction to Deep Networks	AR
Class 13	Thu Oct 21	Deep Learning Theory: Approximation	TP
Class 14	Tue Oct 26	Deep Learning: Optimization and Dynamics	TP
Class 15	Thu Oct 28	Deep Learning: Neural Collapse	AB + AR
Class 16	Tue Nov 02	Group Invariants in Vision	TP+Fabio Anselmi
Class 17	Thu Nov 04	Invariance, Neurons, Synaptic Plasticity, Development	TP+Fabio Anselmi
Class 18	Tue Nov 09	Loose Ends	Staff
Thursday 11th November – Veteran’s Day
Class 19	Tue Nov 16	Neural Networks and the Ventral Stream	TP+ Thomas Serre + Gabriel Kreiman
Class 20	Thu Nov 18	Neural Networks and the Ventral Stream	Thomas Serre + Gabriel Kreiman
Class 21	Tue Nov 23	Graph networks	Stephanie Jegelka
Thursday 25th November – Thanksgiving
Class 22	Tue Nov 30	Statistical inference from dependent samples	Costis Daskalakis
Class 23	Thu Dec 02	Neural Assemblies	Christos Papadimitriou +Santosh Vempala
Class 24	Tue Dec 07	Adversarial examples	Alexander Madry
Class 25	Thu Dec 09	Sample and computational complexity of deep networks	Eran Malach

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.
• T. Hastie, R. Tibshirani and J. Friedman. The Elements of Statistical Learning. 2nd Ed., Springer, 2009.
• I. Steinwart and A. Christmann. Support Vector Machines. Springer, 2008.
• 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.
• N. Cristianini and J. Shawe-Taylor. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, 2000.
• F. Cucker and S. Smale. On The Mathematical Foundations of Learning. Bulletin of the American Mathematical Society, 2002.
• F. Cucker and D-X. Zhou. Learning theory: an approximation theory viewpoint. Cambridge Monographs on Applied and Computational Mathematics. Cambridge University Press, 2007.
• L. Devroye, L. Gyorfi, and G. Lugosi. A Probabilistic Theory of Pattern Recognition. Springer, 1997.
• T. Evgeniou, M. Pontil and T. Poggio. Regularization Networks and Support Vector Machines. Advances in Computational Mathematics, 2000.
• T. Poggio and S. Smale. The Mathematics of Learning: Dealing with Data. Notices of the AMS, 2003.
• V. N. Vapnik. Statistical Learning Theory. Wiley, 1998.
• V. N. Vapnik. The Nature of Statistical Learning Theory. Springer, 2000.
• S. Villa, L. Rosasco, T. Poggio. On Learnability, Complexity and Stability. Empirical Inference: Festschrift in Honor of Vladimir N. Vapnik, Chapter 7, pp. 59-70, Springer-Verlag, 2013.
• 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, A. Banburski, Q. Liao. Theoretical Issues in Deep Networks. PNAS, 1-7, 2020.
• Y. LeCun, Y. Bengio and G. Hinton, Deep Learning, ​Nature, 436-444, 2015.

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.