# Statistical Analysis and Document Mining - 4MMSADM

• #### Number of hours

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

ECTS 3.0

## Goal(s)

The aim of this course is to present statistical approaches for analysing multivariate data. The information age has resulted in masses of multivariate data in many different fields: finance, marketing, economy, biology, environmental sciences,...The theoretical and practical aspects of multivariate data analysis are given equal importance. This balance is achieved through practicals involving actual data analysis using the R software.

This course is intended for students from the IF-mf, ISI and MSIAM students, as well as those within MMIS who are mainly attracted by applications. Students who are attracted by deeper statistical developments regarding the justification of methods are invited to attend "Analyse statistique multidimensionelle" (in French).

Responsible(s)

Pedro Luiz COELHO RODRIGUES

## Content(s)

1. Multiple linear regression. Least squares, Gaussian linear model, test of linear hypotheses, one-way analysis of variance.
2. Principal Components Analysis (PCA).
3. Classification, linear discriminant analysis, perceptron, Naive Bayes
4. Text mining, numeric representation of texts, connexion with graph clustering.

Prerequisites

Elementary notions in probability theory (probability distribution, joint probability density function for random vectors, conditional distribution, expectation, variance, covariance, Gaussian distribution)

Elementary notions in mathematical statistics (estimator, confidence interval, statistical tests). As a bonus: simple linear regression.

Notions in linear algebra (matrix reductions).

As a bonus: elementary notions in Rstudio and the R software.

Test

Practical exam with R (3 h) and reports on supervised practicals.
Authorized document and material: handwritten notes only. An electronic version of the booklet will be provided by the teacher.

• MCC en présentiel **
N1=1/2*TP en temps libre + 1/2*Examen écrit
N2=1/2*TP en temps libre + 1/2*Examen écrit
• 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 2022-2023

Course language(s):

You can find this course among all other courses.

Bibliography

CM BISHOP (2006) Pattern recognition and machine Learning. Springer
http://research.microsoft.com/en-us/um/people/cmbishop/prml/

C. CHATFIELD and AJ COLLINS (1980) Introduction to multivariate analysis. Science paperbacks

T HASTIE, R TIBSHIRANI, and J FRIEDMAN (2009). The Elements of Statistical Learning, 2d ed, Springer. http://www-stat.stanford.edu/~tibs/ElemStatLearn/

G. SAPORTA (2006) : Probabilités, statistique et analyse des données, Technip.