This information is indicative and can be subject to change.
Advanced topics in machine learning
Teacher: Christophe Denis
E-mail:
ECTS: 2.5
Evaluation: Final exam
Previsional Place and time: 9 sessions (2h per session)
Prerequisites: Attended both the first and second semester learning courses and familiar with programming.
Aim of the course:
Syllabus:
The course is focused on Algorithmic fairness in supervised learning.
Algorithmic fairness has become very popular during the last decade.
It helps addressing an important social problem: mitigating historical bias contained in the
data. This is a crucial issue in many applications such as loan assessment, health care, or
even criminal sentencing. The common objective in algorithmic fairness is to reduce the
influence of a sensitive attribute on a prediction.
The objective of the course is to provide a general introduction to algorithmic fairness in the framework of supervised learning (classification and regression). A particular focus will be placed on post-processing approaches.
Advanced topics in machine learning
Teacher: Christophe Denis
E-mail:
ECTS: 2.5
Evaluation: Final exam
Previsional Place and time: 9 sessions (2h per session)
Prerequisites: Attended both the first and second semester learning courses and familiar with programming.
Aim of the course:
Syllabus:
The course is focused on Algorithmic fairness in supervised learning.
Algorithmic fairness has become very popular during the last decade.
It helps addressing an important social problem: mitigating historical bias contained in the
data. This is a crucial issue in many applications such as loan assessment, health care, or
even criminal sentencing. The common objective in algorithmic fairness is to reduce the
influence of a sensitive attribute on a prediction.
The objective of the course is to provide a general introduction to algorithmic fairness in the framework of supervised learning (classification and regression). A particular focus will be placed on post-processing approaches.
