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, emarketing, meteorology, economics, etc, mathematics are useful.
Examples of jobs are provided in the french magazine Zoom des métiers.
PhD supervision
 20142017: Clément Bouttier, industrial PhD at AirbusENACUniversité Paul Sabatier on the topic "Aircraft trajectory modelling and optimization" (cosupervision with Olivier Babando, Sébastien Gadat, Serge Laporte and Florence Nicol). Now working as an engineer at Airbus.
Courses, tutorials, and computer sessions
I am teaching at various levels at Université Toulouse 3  Paul Sabatier. 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]
 Reading advice in probability theory (in french):
 Probabilités, tome I et tome II, JeanYves 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).
 Reading advice in probability theory (in french):
 Mathematical prerequisites for [L3 Statistique et Informatique Décisionnelle]

Together with several colleagues of the SID program, we wrote a document Mathematical prerequisites for L3 SID that you are encouraged to download and read carefully.
 Linear model and design of experiments [L3 Statistique et Informatique Décisionnelle]
MSc. level (Master)
 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 nonconvex case, proof of HoeffdingAzuma's inequality.