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
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
Reference week
| Mo | Tu | We | Th | Fr | |
| 8-9 | RLC E1 240 | ||||
| 9-10 | CM2 CM1105 | ||||
| 10-11 | |||||
| 11-12 | |||||
| 12-13 | |||||
| 13-14 | |||||
| 14-15 | |||||
| 15-16 | |||||
| 16-17 | |||||
| 17-18 | |||||
| 18-19 | |||||
| 19-20 | |||||
| 20-21 | |||||
| 21-22 |
Légendes:
Lecture
Exercise, TP
Project, other
Tuesday, 8h - 10h: Lecture RLC E1 240