Number of hours
- Lectures 24.0
- Projects -
- Tutorials -
- Internship -
- Laboratory works -
- Written tests -
ECTS
ECTS 2.5
Goal(s)
Acquire a general knowledge of data science sufficient to be able to interact with specialists in statistical learning theory.
Responsible(s)
Jean-Marc BROSSIER
Content(s)
- Reminders of linear algebra,
- Minimization of the empirical risk,
- Introduction to the statistical theory of learning,
- Specificities of learning in a "Big Data" context: curse and blessing of the dimension,
- Variety learning,
- Parsimony and penalty,
- Large-scale inference: hypothesis testing and i.i.d. data simulation,
- Control of the rate of false discoveries and correction of multiple tests.
- fundamental concepts of linear algebra
- Euclidean space
- Scalar product
- Basic operations on matrices
- Positive semi-described matrixes
- Hermitian forms
- Matrix diagonalization and eigenvalues
- Indicative bibliography :
- Fabien Margairaz. Algèbre linéaire I & II: Notes de cours de l’EPFL. https://docplayer.fr/23918385-Algebre-lineaire-i-ii.html
- Wikipédia
- fundamental concepts of probability
- Expectation, variance
- Joint and conditional probabilities, Bayes formula
- Usual laws (Bernoulli, uniform, normal)
- Estimation of the parameters using maximum likelihood
- Indicative bibliography :
- Olivier François. Notes de cours de Probabilités Appliquées. Les 40 premières pages. http://membres-timc.imag.fr/Olivier.Francois/Poly_Cours_Proba.pdf
- basic concepts of statistics
- Descriptive statistics : Statistical population, Central tendency and dispersion estimators, Common representations (histogram, bar chart, etc.)
- Elementary notions of hypothesis testing: Samples, Null hypothesis, alternative hypothesis, type I and II risks, Student Test
- Indicative bibliography:
- Olivier Gaudoin. Principes et Méthodes Statistiques : Notes de cours, Ensimag 2A. Chapitres I,II, V (3 premières sections)
https://www-ljk.imag.fr/membres/Olivier.Gaudoin/PMS.pdf
- Olivier Gaudoin. Principes et Méthodes Statistiques : Notes de cours, Ensimag 2A. Chapitres I,II, V (3 premières sections)
Test
N1=E1
N2=E2
N1=E1
N2=E2
Calendar
The course exists in the following branches:
- Curriculum - Big-Data Post-Graduate Program - Semester 9
Additional Information
Course ID : WMMBESDF
Course language(s):
You can find this course among all other courses.
Bibliography
Principes et méthodes statistiques
https://www-ljk.imag.fr/membres/Olivier.Gaudoin/PMS.pdf
Notes de cours de probabilités
http://membres-timc.imag.fr/Olivier.Francois/Poly_Cours_Proba.pdf