Unité pédagogique
From statistics to data mining
Derniere édition le: 26/09/2024
Modifier
Responsable:
Description générale :
Course instructor: Fabrice Muhlenbach
This course counts for 5 credits (/30 total for a semester). It gives the necessary mathematical background to perform data analysis using statistics, linear algebra and convex optimization. Practical sessions make use of the R-free software environment for statistical computing and graphics.
The following topics are covered:
- Basics in probabilities (chance experiments, random variables, moments, law of large number, …)
- Statistics (discrete and continuous distributions, estimates, Maximum Likelihood Estimation,...)
- Basics in linear algebra and in convex optimization
- Linear/polynomial/logistic Regression (closed-form solution, batch and stochastic gradient descent)
- Principal Component Analysis
- Clustering
Study materials:
- Pattern Recognition, S theodoridis, K. Koutroumbas, 4th edition
- Introduction to Statistics and Data Analysis, R. Peck, C. Olsen, J. Devore, Brooks/Cole, 4th edition, 2010.
- Convex Optimization, Stephen Boyd & Lieven Vandenberghe, Cambridge University Press, 2012.
- On-line Machine Learning courses: https://www.coursera.org/ UJM Semester 2 Expected prior-knowledge - Basic mathematics and statistics
Mots-clés:
probability
statistics
linear algebra
optimisation
linear regression
PCA
clustering
Nombre d’heures à l’emploi du temps:
60
Domaine(s) ou champs disciplinaires:
Informatique, Systèmes d'information
Langue d’enseignement:
Anglais
Objectifs d’apprentissage:
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Modalités d’évaluation des apprentissages:
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Part de l'évaluation individuelle
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Part de l'évaluation collective
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Examen sur table :
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66
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%
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Livrable(s) de projet :
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Examen oral individuel :
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Exposé collectif :
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Exposé individuel :
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Exercice pratique collectif :
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Exercice pratique individuel :
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34
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%
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Rapport collectif :
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Rapport individuel :
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Programme et contenus:
| Type d’activité pédagogique : |
Contenu, séquencement et organisation |
| Lectures |
(25h) |
| Supervised work |
(10h) |
| Lab sessions |
(15h) |