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Informatique et Mathématiques appliquées
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> Formation > Cursus ingénieur

Data Science: Algebrical & Statistical Fundamentals - WMMBESDD

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  • Number of hours

    • Lectures : 24.0
    • Tutorials : -
    • Laboratory works : 6.0
    • Projects : -
    • Internship : -
    • Written tests : -
    ECTS : 2.0
  • Officials : Thomas BURGER

Goals

Acquire a general knowledge of data science sufficient to be able to interact with specialists in statistical learning theory.

Content

  • 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.

Prerequisites

  • fundamental concepts of linear algebra
    • Euclidean space
    • Scalar product
    • Basic operations on matrices
    • Positive semi-described matrixes
    • Hermitian forms
    • Matrix diagonalization and eigenvalues
  • fundamental concepts of probability
    • Expectation, variance
    • Joint and conditional probabilities, Bayes formula
    • Usual laws (Bernoulli, uniform, normal)
    • Estimation of the parameters using maximum likelihood
  • 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

Tests

N1=E1
N2=E2

N1=E1
N2=E2

Calendar

The course exists in the following branches:

  • Curriculum - Big-Data Post-Graduate Program - Semester 9
see the course schedule for 2020-2021

Additional Information

Course ID : WMMBESDD
Course language(s): FR

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

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Date of update March 10, 2020

Université Grenoble Alpes