Duration
12h Th, 28h Pr
Number of credits
Lecturer
Yves Brostaux, David Colignon, Benoît Mercatoris, Hélène Soyeurt
Coordinator
Language(s) of instruction
English language
Organisation and examination
Teaching in the second semester
Schedule
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: Basics in Python for Data Science : first tips (H.Soyeurt)
- Module 2: Development and implementation of validation procedures (Y. Brostaux)
- Module 3 : Use of a remote calculation server, management and parallelization of calculations (CECI Consortium) (H. Soyeurt & D. Colignon)
- Module 4: Supervised methods applied to image analysis (B. Mercatoris)
- Module 5: Unsupervised methods applied to spatial clustering (Y. Brostaux)
- Module 6: Deep learning with tensorflow and convolutional neural network (H. Soyeurt)
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%)
Recommended or required readings
The course is given in full English.
All teaching supports are available on e-campus platform.
Exam(s) in session
Any session
- In-person
oral exam
Written work / report
Additional information:
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).
Work placement(s)
Organisational remarks and main changes to the course
Contacts
Hélène Soyeurt Professor 081/62.25.35 hsoyeurt@uliege.be