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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.

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  • Home
  • Courses
  • Timetable
  • Opportunities
  • Applying
  • Contact
  • Internship
  • Optimal transport
  • Algorithmic game theory
  • Neural network