Teaching material and evaluations available on demand. Past teaching activities available in my curriculum.
This page features courses taught at Université de Lorraine for the academic year 2025-2026.
Graduate courses belong to the Statistical Expertise for Economics and Finance master program.
I teach a complete statistical learning track for master students in economics. The course is designed for a total of 75 hours, including practical sessions at the end of each chapter. Chapters are divided into three categories : theory, descriptive methods, predictive methods. Numbers indicate what I believe to be an optimal ordering, which changes every year or so. Students are expected to know basic econometrics, probability theory and statistical inference.
THEORY :
T1 : A crash course in statistical theory (in French) (1)
T2 : Reminders in analysis and algebra (2)
T3 : Introduction to the ERM framework (not written yet) (7)
T5 : Numerical optimization (10)
DESCRIPTIVE METHODS :
D1 : Dimensionality reduction (3)
D2 : Clustering (5)
D3 : Statistical analysis of network data (6)
PREDICTIVE METHODS :
P1 : Logistic regression (4)
P2 : Feature selection and validation (8)
P3 : Proximity-based learning (11)
P4 : More linear models for prediction (12)
P5 : Tree-based methods and ensemble learning (13)
P6 : Kernel methods and SVM (14)
PRACTICAL SESSIONS:
Session 1: My first facial recognition algorithm ! (Dimensionality reduction + ethics)
Session 2: How to optimize my Internet scam ? (Clustering)
Session 3: Selling Sunset ! (Feature selection + hedonic regression + data collection ethics)
Session 4: The Facebook + Eurovision is so biased these days (Network data)
Session 5: Public transportation (Multinomial classification)
Session 6: Where's Waldo ? Spot the frauder ! (Ensemble methods + resampling)
This as a graduate course in digital economics designed for Law and Economics students. Prerequisites include basic microeconomics and IO. The mathematical content is voluntarily kept at a minimum. All the material is in French and designed for 20 hours of lectures (10x2h sessions). Reading material include the Goldfarb & Tucker (2019) survey and a selection of articles mentioned in each session.
Chapter 2: Informational goods and replication costs
Chapter 3: Consumers and transaction costs
Chapter 4: Intermediation, matching and market design
Chapter 5: Platform strategies
Chapter 6: Data, pricing and auctions
Chapter 7: Information and reputational effects
Chapter 8: Recommender systems
Chapter 9: Market concentration and dynamics (not written yet)
Chapter 10: AI, tech funding and venture capitalism (not written yet)
This course is an introduction to economic design via traditionnal information economics.
Chapter 1 : Decision theory and microeconomic modelling
Chapter 2 : Non-strategic markets
Chapter 3 : Game theory
Chapter 4 : Oligopolies
Chapter 5 : Collusion
Chapter 6 : Innovation, entry and exists
Chapter 1: From perfect competition to monopolies
Chapter 2: Price discrimination
Chapter 3: Oligopolies revisited
Chapter 4: Differenciation
Chapter 5: Vertical relationships