Program

I. Introduction to machine learning

• A mathematical theory of induction and empirical risk minimization
• Problem formulation for supervised learning

II. Generalization guarantees 

• Assessing learning performance. The problem of overfitting
• Performance bounds based on uniform convergence of averages. Union bound and Rademacher complexity

Bias in Machine Learning: the mathematical perspective (3h theory) - Jean-Michel Loubes (INRIA (Institut de Mathématiques de Toulouse)

I. A brief survey of Bias in AI

1. Bias definitions

2. What tells the law and the regulation

II. The origin of bias

1. Bias in the data

2. Bias in the algorithm

III. Measures of Bias

IV. Some useful tools: Optimal Transport

Bias in Machine Learning: the mathematical perspective (3h theory) - Jean-Michel Loubes (INRIA (Institut de Mathématiques de Toulouse)

V. Applications of OT to Fairness

1. Detection

2. Bias mitigation

2.1 a posteriori

2.2 a priori

Bias in Machine Learning: the mathematical perspective (4h theory) - Jean-Michel Loubes (INRIA (Institut de Mathématiques de Toulouse))

VI. Explaining Bias

VII. Audit of an algorithm