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PA - C8 - MAP670F : Theorical Guidelines for High-dimensional Data Analysis

Domaine > Mathématiques appliquées.

Descriptif

Program

Goal of the lectures:

  • to draw your attention to some issues in data analysis, and some proposals to handle them;
  • to learn to read a research paper, to catch the take home message and to identify the limits;
  • to favor your own critical analysis.

The lecture will be based on some recent research papers. The presence during the lectures is mandatory and taken into account in the final evaluation.

Schedule

Lecture

Topic

Paper(s)

Slides

Further reading

1

False discoveries, multiple testing, online issue

paper 1 (short review)

Slides

Reliability of scientific findings? Online FDR control

2

Strength and weakness of the Lasso

Paper 1

Slides

No free computationnal lunch

3

Adaptive data analysis

Paper 1

Slides

Kaggle overfiting

4

Curse of dimensionality, robust PCA, theoretical limits

Paper 1 (suppl. material)

Slides

Robust PCA

5

Robust learning

Paper 1

Slides

Learning with Median Of Means

Diplôme(s) concerné(s)

Format des notes

Numérique sur 20

Littérale/grade réduit

Pour les étudiants du diplôme Data Sciences

Le rattrapage est autorisé (Max entre les deux notes)
    L'UE est acquise si Note finale >= 10
    • Crédits ECTS acquis : 2.5 ECTS

    Pour les étudiants du diplôme Echanges PEI

    Le rattrapage est autorisé (Max entre les deux notes)
      L'UE est acquise si Note finale >= 10
      • Crédits ECTS acquis : 2.5 ECTS
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