Class | Date | Title | Instructor(s) |
---|---|---|---|

Class 01 | Thu Sep 05 | Course Outline + Statistical Machine Learning | TP + LR |

Class 02 | Tue Sep 10 | Regularized Least Squares | LR |

Class 03 | Thu Sep 12 | Logistic Regression and SGD | LR |

Class 04 | Tue Sep 17 | Implicit Regularization | LR |

Class 05 | Thu Sep 19 | Neural Networks - Introduction | LR |

Class 06 | Tue Sep 24 | Kernels and Feature Maps | LR |

Class 07 | Thu Sep 26 |
Kernel Regression |
LR |

Class 08 | Tue Oct 01 |
Statistical Learning Theory |
LR |

Class 09 | Thu Oct 03 | Stability | LR |

Class 10 | Tue Oct 08 | Deep Learning: a special case of classical theory? | TP |

Class 11 | Thu Oct 10 | Deep Learning: Universal Approximation | TP |

Tuesday, October 15 - No Class - Student Holiday |
|||

Class 12 | Thu Oct 17 | Deep Learning: Approximation - Compositional Sparsity | TP |

Class 13 | Tue Oct 22 |
Deep Learning: Optimization |
TP |

Class 14 | Thu Oct 24 |
Deep Learning: Optimization - Neural Collapse |
TP |

Class 15 | Tue Oct 29 | Deep Learning: Optimization - SGD Noise+Low Rank | TP |

Class 16 | Thu Oct 31 | Deep Learning: Generalization | TP |

Class 17 | Tue Nov 05 | Deep Learning: Transfer Learning |
ML |

Class 18 | Thu Nov 07 | Deep Learning: Transformers | TP |

Class 19 | Tue Nov 12 | Deep Learning: training samples from the "human distribution" | ML |

Class 20 | Thu Nov 14 | Statistical learning and scientific models in cognitive neuroscience | ML |

Class 21 | Tue Nov 19 | Guest Lecture | TBD |

Class 22 | Thu Nov 21 | Guest Lecture | TBD |

Class 23 | Thu Nov 26 | Guest Lecture | TBD |

Thursday, November 28 - No Class -Thanksgiving |
|||

Class 24 | Tue Dec 03 | Guest Lecture | TP |

Class 25 | Thu Dec 05 | Guest Lecture | TBD |

Class 26 | Tue Dec 05 | Guest Lecture | TBD |

**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. - 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. - F. Cucker and S. Smale.
**On The Mathematical Foundations of Learning**. Bulletin of the American Mathematical Society, 2002. - 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. - 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.