# Statistical learning with applications - 4MMASA

• #### Number of hours

• Lectures 16.5
• Projects -
• Tutorials 9.0
• Internship -
• Laboratory works 9.0
• Written tests -

ECTS 3.0

## Goal(s)

The goal of this course is to introduce students to classical approaches of statistical learning. The information age has resulted in masses of multivariate data in many fields: finance, marketing, economy, biology, environmental sciences, and the knowledge to handle them in a rigorous and self-critical manner is of great importance in research and industry. We will give equal importance to theoretical and practical aspects of statistical learning, showing several applications in class and proposing practical sessions in which the student has to perform actual data analysis using the R software.

This course is intended for students from IF, ISI, and MMIS, as well as those form the M1AM master.

Responsible(s)

Pedro Luiz COELHO RODRIGUES

## Content(s)

Review of multivariate statistics. Simple and multivariate linear regression. Cross-validation, model selection, bias-variance. Principal component analysis. Linear classification: discriminative and generative approaches. Decision trees. Ensemble methods: bagging and boosting. Performance metrics and overfitting. Introduction to network analysis and community detection in graphs.

Prerequisites

Notions of probability theory: probability distribution, joint probability density function for random vectors, conditional distribution, expectation, variance, covariance, Gaussian distribution

Notions of mathematical statistics: estimator, confidence interval, statistical tests.

Notions of linear algebra: matrix reductions, eigenvalue decomposition.

Bonus: Elementary notions of R programming, writing Rmd files, and typesetting equations in LaTeX.

Test

Practical exam using R (3h) and reports on supervised practical sessions.

Documents authorized in the final exam: handwritten notes only. All slides, lecture notes, and relevant books will be available in the computers.

• MCC en présentiel **
N1=1/2*TP en temps libre + 1/2*Examen pratique
N2=1/2*TP en temps libre + 1/2*Examen pratique
• MCC en distanciel**
N1=1/2*TP en temps libre + 1/2*Devoir à la maison
N2=1/2*TP en temps libre + 1/2*Devoir à la maison

The exam is given in english only

Calendar

The course exists in the following branches:

• Curriculum - Math. Modelling, Image & Simulation - Semester 8 (this course is given in english only )
• Curriculum - Information Systems Engineering - Semester 8 (this course is given in english only )
• Curriculum - Financial Engineering - Semester 8 (this course is given in english only )
see the course schedule for 2023-2024