High-dimensional data analysis

Duration

30h Th, 15h Pr, 30h Proj.

Number of credits

Lecturer

Gentiane Haesbroeck

Language(s) of instruction

English language

Organisation and examination

Teaching in the first semester, review in January

Schedule

Schedule online

Units courses prerequisite and corequisite

Prerequisite or corequisite units are presented within each program

Learning unit contents

The course is devoted to the following themes:
- Exploratory data analysis - Multivariate distributions (including the normal distribuion), point estimation (ML technique) and elements of inferential statistics - Estimation of the covariance matrix: classic technique, penalized version and robust version - Dimension reduction technique: Principal Component Analysis, Multidimensionla Scaling, tSNE, Independent Component Analysis - Supervised classification: discriminant analysis and logistic regression - Multivariate ranks and quantiles

Learning outcomes of the learning unit

The student will gain sufficient knowledge to be able to select the appropriate multivariate technique to reduce the dimension of the problem or construct classification rules,...

Prerequisite knowledge and skills

A strong background in univariate statistics is required. Moreover, even though the mathematical justifications are not developped in details, the students must be familiar with the basic notions of linear algebra (vector, matrix, detemrinant, eigen valeurs and eigen vectors...).

Planned learning activities and teaching methods

Practicals include data analysis and simulations in order to compare techniques, using the the statistical package R.

Mode of delivery (face-to-face ; distance-learning)

The course is officially scheduled on Wednesday PM in the first semester. 

Recommended or required readings

There are no lecture notes. The slides will be available from eCampus. Moreover, for each theme, a reference book will be notified in order to suggest additionnal reading.
 

Assessment methods and criteria

The final grade is a weighted mean computed on the grades obtained for
- the personal homeworks given during the semester
- the written exam consisting of a data analysis and simulations 

Work placement(s)

Organizational remarks

The lectures are taught in English.

Contacts

Lecturer: Gentiane HAESBROECK, Institute of Mathematics (B37), g.haesbroeck@ulg.ac.be

Adaptation of teaching commitments following the COVID-19 pandemic for the May-June 2020 session

Teaching methods implemented : distance-learning

Assessment subjects

Assessment methods

Contacts

Adaptation of teaching commitments following the COVID-19 pandemic for the Aug-Sept 2020 session

Assessment subjects

Same content as in January.
It is possible de keep the overal grade of the projects if it is equal to or bigger than 10/20. Otherwise, new project statements will be sent out.

Assessment methods

Distance written exam.
 
The questions will be sent by email at 9 am on the day of the exam and the written resolution (together with the R code and the figures) will need to be submitted by email by 1 pm on the same day.

Contacts