Elements of statistics for data science
Contenu
- Théorie de l'estimation: estimateurs du maximum de vraisemblance et des moments, information de Fisher, inégalité de Cramer-Rao, intervalles de confiance exacts et asymptotiques, bootstrap.
- Tests d'hypothèses: cadre de Neyman-Pearson, test du rapport de vraisemblance, tests paramétriques (t-tests, z-tests, tests du chi2) et non-paramétrique (Wilcoxon)
- Introduction à l'inférence bayésienne: a priori, a posteriori, distribution prédictive, a priori conjugé,exemples de mise en oeuvre, intervalles de crédibilité.
- Modèle linéaire: théorème de Gauss-Markov, cas Gaussien, modèle linéaire généralisé
- Analyse en Composantes Principales
Ressources
Références suggérées par la bibliothèque
Liens Moodle
In the programs
- Semester: Spring
- Exam form: Written (summer session)
- Subject examined: Elements of statistics for data science
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 1 Hour(s) per week x 14 weeks
- Semester: Spring
- Exam form: Written (summer session)
- Subject examined: Elements of statistics for data science
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 1 Hour(s) per week x 14 weeks
- Semester: Spring
- Exam form: Written (summer session)
- Subject examined: Elements of statistics for data science
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 1 Hour(s) per week x 14 weeks
- Semester: Spring
- Exam form: Written (summer session)
- Subject examined: Elements of statistics for data science
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 1 Hour(s) per week x 14 weeks
Reference week
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Légendes:
Lecture
Exercise, TP
Project, other