

Machine Learning, DDEFI track
Final presentation with the code (50%): the delivered project
Until the day before the final presentation (Thursday 11 and Friday 12 February 2027 block, exact day to be confirmed), in teams of two to four, preferably three; final presentation with the code, 50% of the grade, homework (final delivery)
Centrale Méditerranée, Château-Gombert technology park, Marseille
Each session has its own password, given in class. Capitals, spaces and hyphens do not matter. The slides are encrypted: nothing can be read without it.
Keep a note under each slide (key N, or the Notes button). With a Google account they follow you on every device; without one they stay in this browser.
Notes are stored on your account only; you can delete them at any time from the slides. Privacy
| Session | Title | Date | |
|---|---|---|---|
| Introduction | AI at a crossroads: history, troughs, jobs, and you | 14 October 2026 | Open |
| Tools | Kaggle, GitHub, Python: your tools, your accounts, your repository | 14 October 2026 | Open |
| Session 1 | Frame before you model, then least squares | 14 October 2026 | Open |
| Session 2 | Likelihood, generalisation, project kick-off | 14 October 2026 | Open |
| Session 3 | Convex optimisation and regularisation | 15 October 2026 | Open |
| Session 4 | Decide under cost, ship a reproducible repository | 15 October 2026 | Open |
| Homework 1 | Milestone 1: your project and its problem statement (graded out of 5) | Milestone 1 (graded out of 5) | Open |
| Session 5 | Finishing Titanic together, kernels, SVM and decision trees | 15 December 2026 | Open |
| Session 6 | Forests, boosting and the honest comparison | 16 December 2026 | Open |
| Session 7 | Explain, audit fairness, discover without labels | 16 December 2026 | Open |
| Homework 2 | Milestone 2 (25%): an OLS or a simple classification on your project | Milestone 2 (25%) | Open |
| Session 8 | Neural networks, drift, conformal prediction, going live | 12 February 2027 | Open |
| Homework 3 | Final presentation with the code (50%): the delivered project | Final presentation | |
| Session 9 | Final presentation day: the defences | 11 or 12 February 2027 | Open |
| Thread | The running thread, step by step: the default-score repository, in screenshots | Reference | Open |
| Example | Predicting an asset price: time series, XGBoost and news sentiment | Reference | Open |
| The code | The code of every model: import, download by API, fit, predict | Reference | Open |
| Projects | Open data and machine learning projects by sector: finance, insurance, logistics, tech, sport | Reference | Open |
| Handbook | The data and AI expert handbook: scope, tool up, model, deploy, observe | Reference | Open |
| Infra | Data engineering, SRE and cloud: the commands, the principles, the jobs, from junior to expert | Reference | Open |
| Panorama | A panorama of models: which ones, what for, in which sectors | Reference | Open |
| Deep learning | Deep learning, the essentials: key concepts, why they matter, the maths on the board, and a mini tutorial | Reference | Open |
Table 1: the sessions of the course, in order; each one opens with its own password.