The class will be taught
in French or English, depending on attendance (all slides and class
notes are in English).

Summary

Unlike a course on traditional statistics, statistical machine learning is particularly focused on the analysis of data in high dimension, as well as the efficiency of algorithms to process the large amount of data encountered in multiple application areas such as image or sound analysis, natural language processing, bioinformatics or finance.

The objective of this class is to present the main theories and algorithms in statistical machine learning, with simple proofs of the most important results. The practical sessions will lead to simple implementations of the algorithms seen in class.

Date

Given the
sanitary situations, we will use an online "flipped classroom"
methodology. For every lecture, sections of the book
in preparation (check regularly for latest versions) will be
highlighted. Students are expected to study the material ***before***
Friday morning. The friday morning online session will be divided
in three groups (each group with a third of students) and students
will have the opportunity to ask questions after the lecturer
provides a quick overview of the material. **Each student has to
ask at least one question**. Practical sessions will be done
at home.

**Homeworks**

Please send the practical sessions (one jupyter notebook .ipynb
with cells containing either text or runnable code) to
lenaicfrancisml@gmail.com with the subject [PSn] with n being the
number of the practical session (no acknowledgements will be sent
back).

Lecturer | Date | Topics | Book
sections |

LC | 15 January | Introduction to supervised learning (loss, risk, over-fitting and capacity control + cross-validation, Bayes predictor for classification and regression | 1.2.1,
1.2.4 2.1, 2.2, 2.3, 2.4 |

FB | 22 January | Least-squares regression (all aspects, from linear
algebra to statistical guarantees and L2 regularization +
practical session) Practical session 1, due February 12, 2021 (mnist_digits.mat) |
3.1, 3.2, 3.3, 3.4, 3.5, 3.6 |

LC | 29 January | Statistical ML without optimization (learning theory, from finite number of hypothesis to Rademacher / covering numbers) | 4.1.1, 4.1.2, 4.2, 4.3, 4.4.(1-3), 4.5.(1-4) |

FB | 5 February | Convex optimization (gradient descent + nonsmooth +
stochastic versions) Practical session 2, due March 5, 2021 |
5.1, 5.2.1, 5.2.2, 5.2.3, 5.3, 5.4 (not 5.4.1 and 5.4.2) |

LC | 12 February | Local averaging techniques (K-nearest neighbor,
Nadaraya-Watson regression: algorithms + statistical analysis) Practical session 3, due March 12, 2021 |
6 (all sections except the diamond ones) |

FB | 19 February | Kernels (positive-definite kernels and reproducing kernel Hilbert spaces) | 7 (all sections except the diamond ones) |

26 February | Holidays | ||

LC | 5 March |
Model selection (feature selection, L1
regularization and high-dimensional inference + practical
session). Practical session 4, due April 2, 2021 |
8 (all sections except the diamond ones) |

FB | 12 March | Neural networks (from one-hidden layer to deep
networks) Practical session 5, no need to return it |
9 (all sections except the diamond ones) |

LC | 19 March | Special topics |
10 |

FB | 26 March | Review | |

2-4 April | Final homework |

Evaluation

**
**

-Practical sessions to do at home and to be sent to lenaicfrancisml@gmail.com

-Final
homework at the end of the class

** **