MATH-804 / 2 credits

Teacher: Davison Anthony Christopher

Language: English

Remark: Registration via website https://mlstats2022.epfl.ch/


Frequency

Only this year

Summary

ML for predictive modeling is important in both industry and research. We join experts from stats and math to shed light on particular aspects of the theory and interpretability of DL. We discuss the statistical theory and generalization behavior of deep NNs, and how to move towards trustworthy DL.

Content

Keywords

statistics, statistical learning, optimization, deep learning, regression, interpretability

In the programs

  • Exam form: Project report (session free)
  • Subject examined: MLSTATS
  • Lecture: 16 Hour(s)
  • Practical work: 16 Hour(s)

Reference week