2023-2024 / INFO8008-1

Multivaried analysis 2: data mining and machine learning

Duration

12h Th, 26h Pr

Number of credits

 Master in agricultural bioengineering (120 ECTS)4 crédits 
 Master in bioengineering : chemistry and bio-industries (120 ECTS)4 crédits 
 Master in environmental bioengineering (120 ECTS)4 crédits 
 Master in forests and natural areas engineering (120 ECTS)4 crédits 

Lecturer

Yves Brostaux, Juan Antonio Fernandez Pierna, Hélène Soyeurt

Coordinator

Hélène Soyeurt

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 divided into 6 learning modules including one face-to-face session and e-learning activities:

  • Module 1: Linear, Ridge and Lasso regressions
  • Module 2: Principal component regression (PCR) + Partial least square regression (PLS)
  • Module 3: Logistic regression
  • Module 4: Random forest
  • Module 5: PLS - discrniminant analysis + Super vector machine (SVM)
  • Module 6: Neural network

Learning outcomes of the learning unit

After this course, the student will be able to conduct a complete exploratory data analysis from the data cleaning to the practical implementation.
The student will be also able to communicate the obtained results to stakeholders.

Prerequisite knowledge and skills

STAT2002-A-a : Statistique fondamentale, 1ère partie
STAT2004-A-a : Statistique appliquée : 1ère partie
STAT2005-A-a : Statistique appliquée : 2ème partie
STAT1213-A-a : Analyse statistique à plusieurs variables

Planned learning activities and teaching methods

The course is composed of 6 modules as aformentionned. Each module includes:

  • one face-to-face session (2h) developping the theoritical concepts
  • one e-learning session (1h) applying in practice the exposed theoritical concepts
  • one e-learning session (3h) based on the resolution of a full data analysis dedicated to the exposed theoritical concepts

Mode of delivery (face to face, distance learning, hybrid learning)

Face-to-face session (30%) + e-learning activities (70%)

Recommended or required readings

The course is given in full English.
All course supports are available on e-campus platform.

Exam(s) in session

Any session

- In-person

oral exam

Written work / report


Additional information:

The evaluation during the exam session will consist of:





  • answering questions related to the course content (30 min)
  • an oral assessment related to the work given one month before the evaluation (15 min).
Yellow or orange code: the exam will be realized orally in the Statistics Unit. All sanitary rules will be taken.

 

Work placement(s)

Organisational remarks and main changes to the course

Contacts

Hélène Soyeurt
Chargé de cours
081/62.25.35
hsoyeurt@uliege.be

Association of one or more MOOCs