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Centrale Méditerranée · DDEFI option · 2026–2027

Machine Learning

We spend hours deriving gradients, yet the course starts elsewhere: the decision someone has to make, the target, the metric, the guard-rails. Only then the maths, checked in code, on a real credit-default score.

  • 54 hours in three two-day blocks
  • October, December, January
  • Taught in French
  • Technopôle de Château-Gombert, Marseille
A Galton board: the balls fall into a normal distribution
Photo: Matemateca (IME/USP)/Rodrigo Tetsuo Argenton, CC BY-SA 4.0, Wikimedia Commons

Block 1: the four half-days of 14 and 15 October

  1. Half-day 1: Cadrer avant de modéliser, puis les moindres carrés

    Wednesday 14 October 2026 · 08:30–12:30 · on campus

  2. Half-day 2: Vraisemblance, généralisation, lancement du projet

    Wednesday 14 October 2026 · 13:30–18:30 · on campus

  3. Half-day 3: Optimisation convexe et régularisation

    Thursday 15 October 2026 · 08:30–12:30 · on campus

  4. Half-day 4: Décider sous coût, livrer un dépôt reproductible

    Thursday 15 October 2026 · 13:30–18:30 · on campus

The slides open with the session password. On a laptop they display in reading mode with the notes under each slide, so an absent student can follow.

How it works

  1. 1

    Frame first

    Decision, question, target, business metric and model metric, guard-rails, working frame, data pipeline. Before any formula.

  2. 2

    Derive, then check

    Every session starts from a derivation and ends with code that reproduces it and compares it with scikit-learn.

  3. 3

    One project all the way

    A credit-default score on open data, in teams of three: calibrated, thresholded by cost, tested, served behind an API.

  4. 4

    AI allowed, and declared

    For the plumbing, yes. The grade rests on what you can derive and explain without it: 55 % in class, without AI.

The full programme

All 26 sessions, the milestones, the grading grid and the AI policy are on the course companion page, with four field pages: ML in finance, generative AI, the jobs, and the basics in animations.

See the programme on ecofinlearning.com →

The lecturer

Sitraka Forler works with data and systems every day in industry (Enterprise System Architect, POST Luxembourg) and teaches at Centrale Méditerranée, Aix-Marseille School of Economics, IAE Metz, the Université Catholique de Lille and Durham University Business School.

Track record →About →