## Teaching

### Good news for math students: lots of jobs!

The number of jobs that a math student can apply to is huge. The reason is that mathematics bring advanced concepts and rigorous methods for modelling, forecasting, optimization and automatic data processing. Whether you want to work in biostatistics, aeronautics, e-marketing, meteorology, economics, etc, mathematics are useful.

Examples of jobs are provided in the french magazine Zoom des métiers.

### PhD supervision

- 2014-2017: Clément Bouttier, industrial PhD at Airbus-ENAC-Université Paul Sabatier on the topic "Aircraft trajectory modelling and optimization" (co-supervision with Olivier Babando, Sébastien Gadat, Serge Laporte and Florence Nicol). Now working as an engineer at Airbus.

### Courses, tutorials, and computer sessions

I am currently in charge of the following courses and tutorials. I will add some supplementary materials below during the year.

#### BSc. level (Licence)

- Mathematical modeling [L2 Mathématiques]
- Introduction to statistics [L2 2B2M]
- Codes R : Introduction

- Stochastic simulations [L3 MApI3 and Magistère Economiste Statisticien]
- Reading advice in probability theory (in french):
- Probabilités, tome I et tome II, Jean-Yves Ouvrard.
- Exercices de probabilités : licence, master, écoles d'ingénieurs, Marie Cottrell et al.
- Cours de Licence d'Olivier Garet.
- Initiation aux probabilités, Sheldon M. Ross (sans théorie de la mesure).

- Solutions for TD0, exercises 4, 5, and 6.
- Solutions for TD1, exercise 2, exercise 3, and exercises 4 and 7.
- Solutions for TD3, exercise 7.
- Solutions for TD4 (earlier version, so some notation are not up to date).
- Solutions for TD5, exercise 3 and TD6, exercise 2.
- Solutions for TD7, exercise 1.

- Reading advice in probability theory (in french):
- Exploratory data analysis [L3 Statistique et Informatique Décisionnelle]
- TP3: download the solution template.

- Linear model and design of experiments [L3 Statistique et Informatique Décisionnelle]

#### MSc. level (Master)

- Mathematical foundations of deep learning [Master MVA]
- The slides will be available on that webpage.

- Introduction to bandits [M2 MApI3]
- A short introduction to bandits: lecture notes and python notebook.

- Mathematics of machine learning [M2R MFA], with Aurélien Garivier (in 2016 and 2017).
- Nonparametric density estimation: lecture notes (see also Arnak Dalalyan's webpage).
- Minimax lower bounds: lecture notes, appendix, and exercises.
- Introduction to bandit models: lecture notes.
- Supervised classification: complements (reading the minimax lower bound is a good exercise).
- Learning and optimization: lecture notes.
- Online prediction with expert advice: lecture notes, complement on the non-convex case, proof of Hoeffding-Azuma's inequality.