Table of Contents
Stochastic calculus for Machine Learning
Registration to the course
Program of the course
- Where? ENS Paris-Saclay.
- When?
| Topics | Material | |
|---|---|---|
| 1 | Brownian motion and the Wiener space: defintions and first properties | TD 0 and TD 1 and notes |
| 2 | Brownian motion: Markov property and further properties | TD 3 and notes |
| 3 | End Chapter 2. Filtration and measurability. Starting continuous martingales | complete notes chapter 2 and notes chapter 3 |
| 4 | Continuous martingales – Bounded variation processes | notes chapter 4 TD3 |
| 5 | Local martingales and their bracket | notes chapter 5 TD4 |
| 6 | Stochastic integration with respect to semi-martingales | notes End chapter 5 notes chapter 6 TD5 |
| 7 | Ito formula | notes chapter 7 TD6 |
| 8 |
Evaluation
Contact
| name | email adresses |
|---|---|
| Alain Durmus | alain.durmus “Arobase” polytechnique.edu |