Follow the link for each class to find a detailed description, suggested readings, and class slides.
Notes covering the classes are provided in the form of independent chapters of a book currently in draft format. Additional information id 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 are also provided separately for each class.
- L. Rosasco and T. Poggio, Machine Learning: a Regularization Approach, MIT-9.520 Lectures Notes, Manuscript, Dec. 2017 (provided).
- 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 and F. Anselmi. Visual Cortex and Deep Networks: Learning Invariant Representations, Computational Neuroscience Series, MIT Press, 2016.
- 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 and Q. Liao. Theory II: Landscape of the Empirical Risk in Deep Learning. CBMM Memo 66, 2017.