Duration
20h Th, 10h Pr, 10h Mon. WS
Number of credits
| Bachelor of Science (BSc) in Computer Science | 5 crédits | |||
| Bachelor in mathematics | 4 crédits |
Lecturer
Coordinator
Language(s) of instruction
French 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 covers the following topics:
- Markov Chains (Definition, transition matrix and graph, state classifications, asymptotic behavior, mean time to first passage or return)
- Poisson Processes
- Markov Processes
- Queuing Theory
Learning outcomes of the learning unit
After the course, students will master the main properties of most classical stochastic processes.
Prerequisite knowledge and skills
good understanding of concepts in probability theory, matrix calculus, integral calculus, and graph theory.
Planned learning activities and teaching methods
In addition to the traditional classroom course, the course includes 10 hours traditional exercise sessions (10h Pr, ex cathedra).
Students also have 10 hours of personal research work (10h TD). This work will be carried out in groups, and the guidelines will be provided during the theoretical class.
Mode of delivery (face to face, distance learning, hybrid learning)
Recommended or required readings
Course notes are available through eCampus.
Bibliography
- Norris, James R. (1998). Markov chains. Cambridge University Press.
- Ross, Sheldon (2006). Introduction to probability models. Academic Press.
Any session :
- In-person
written exam ( open-ended questions )
- Remote
written exam ( open-ended questions )
- If evaluation in "hybrid"
preferred in-person
Additional information:
The final grade will be a weighted average of two grades :
- that obtained after a written exam held in June (concerning both theory and exercises);
- the grade obtained after evaluation of a project.
Work placement(s)
Organisational remarks and main changes to the course
Contacts
Theory: C. Esser (Celine.Esser@uliege.be)
Exercises: A. Molla (A.Molla@uliege.be)
Project: P. Geurts (P.Geurts@uliege.be)