INFO9013-1

Duration

12h Th, 24h AUTR

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, Benoît Mercatoris, Hélène Soyeurt

Coordinator

Hélène Soyeurt

Language(s) of instruction

English language

Organisation and examination

Teaching in the second semester

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 modules (2h of face-to-face course with podcast + 4h of e-learning activities):

Module 1: Python initiation
Module 2: Unsupervised methods with Python (direct link with the course "Multivaried analysis 2: Data Mining & Machine Learning")
Module 3: Supervised methods with Python (direct link with the course "Multivaried analysis 2: Data Mining & Machine Learning")
Module 4: Use of servers and parallel computing
Module 5: Development and implementation of validation procedures with Python
Module 6: Artificial neural network - advanced level

Learning outcomes of the learning unit

At the end of this course, the student will be able to conduct data analysis using Python from the calibration to the validation.
The student will be also able to communicate the results to the stakeholders.

Prerequisite knowledge and skills

INFO8008-A-a: Multivaried analysis 2: Data Mining & Machine Learning

Planned learning activities and teaching methods

The course is composed of 6 modules as mentioned previously. Each module is composed of:

2h of face-to-face course to learn the theoretical concepts
4h of e-learning activities

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

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

Organisational adjustments related to the current health context

The evaluation will be entirely done virtually.

Assessment methods and criteria

Below you will find information on the evaluation methods planned for in-person and remote exams as well as those planned for hybrid sessions. Depending on how the health crisis evolves, the chosen method will be communicated to you no later than one month before the start of the exam session.

The evaluation is scheduled during the exam session into 2 parts:

writing answers to questions related to the theory (30min)
oral exam related to a work given one month before the evaluation (15 min).

Yellow or orange code: the oral exam will be done at the Statistical Unit in agreement with the sanitary rules.

Work placement(s)

Organizational remarks

Contacts

Hélène Soyeurt Professor 081/62.25.35 hsoyeurt@uliege.be

Multivaried analysis 3: Data mining et Machine Learning: advanced