15h Th, 15h Pr, 5h Proj.
Number of credits
|Bachelor in computer science||3 crédits|
|Master in data science (120 ECTS)||3 crédits|
|Master of science in computer science and engineering (120 ECTS)||3 crédits|
Language(s) of instruction
Organisation and examination
Teaching in the first semester, review in January
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
Basic notions of probability theory: combinatorics, discrete random variables, continuous random variables (basics), probability laws, limit theorems.
Learning outcomes of the learning unit
The student will be able to recognize elementary probabilistic problems and the corresponding discrete random variables. He will solve these problems and write the necessary computer programs.
Prerequisite knowledge and skills
Elementary computer programming.
Elementary algebra, elementary calculus.
Planned learning activities and teaching methods
Theoretical lectures and supervised problem solving sessions.
Mode of delivery (face-to-face ; distance-learning)
Face-to-face. Tuesday at 8:15, R21.
Recommended or required readings
A First Course in Probability, by Sheldon Ross, 8th or 9th edition.
See also http://www.montefiore.ulg.ac.be/~gribomon/cours/cours.html
Assessment methods and criteria
Programming project. Written examination in January.