Duration
General course : 24h Th, 12h Pr, 10h Proj.
Project complement : 30h Proj.
Number of credits
| Master of Science (MSc) in Data Science | 5 crédits | |||
| Master of Science (MSc) in Data Science and Engineering | 5 crédits |
Lecturer
General course : Arnout Van Messem
Project complement : Arnout Van Messem
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
General course
1 Introduction
2 Models and challenges
3 Generating random variables
4 Generating random processes
5 Monte Carlo Integration and Optimization
6 Markov Chain Monte Carlo
7 Statistical analysis of simulation data
8 Variance reduction
Project complement
1 Introduction
2 Models and challenges
3 Generating random variables
4 Generating random processes
5 Monte Carlo Integration and Optimization
6 Markov Chain Monte Carlo
7 Statistical analysis of simulation data
8 Variance reduction
Learning outcomes of the learning unit
General course
Good understanding of the problematics related to simulation and sampling.
Project complement
Good understanding of the problematics related to simulation and sampling.
Prerequisite knowledge and skills
General course
To follow this course it is mandatory to have solid foundations in
- probability theory (probability measure, probability distributions both uni and multi-variate, CLT, Law of large numbers, ...)
- parametric statistics (likelihood, fisher information, statistical tests, confidence intervals, ...)
Reference for the basics : Casella, George, and Roger L. Berger. Statistical inference. Vol. 2. Pacific Grove, CA: Duxbury, 2002.
Project complement
To follow this course it is mandatory to have solid foundations in
- probability theory (probability measure, probability distributions both uni and multi-variate, CLT, Law of large numbers, ...)
- parametric statistics (likelihood, fisher information, statistical tests, confidence intervals, ...)
Reference for the basics : Casella, George, and Roger L. Berger. Statistical inference. Vol. 2. Pacific Grove, CA: Duxbury, 2002.
Planned learning activities and teaching methods
General course
Ex cathedra teaching, exercise sessions (both on computer and on paper). An end of term assignement is planned, though the precise modalities still need to be fixed.
Project complement
Ex cathedra teaching, exercise sessions (both on computer and on paper). An end of term assignement is planned, though the precise modalities still need to be fixed.
Mode of delivery (face to face, distance learning, hybrid learning)
General course
face-to-face
Project complement
face-to-face
Organisational adjustments related to the current health context
General course
distance learning
Recommended or required readings
General course
All information (course notes, project and exercise sheets) will be made available via the eCampus platform.
Références
Kroese, Dirk P., Thomas Taimre, and Zdravko I. Botev. Handbook of monte carlo methods. Vol. 706. John Wiley & Sons, 2013.
Robert, Christian, and George Casella. Monte Carlo statistical methods. Springer Science & Business Media, 2013.
Robert, Christian P., George Casella, and George Casella. Introducing monte carlo methods with r. Vol. 18. New York: Springer, 2010.
Project complement
All information (course notes, project and exercise sheets) will be made available via the eCampus platform.
References
Kroese, Dirk P., Thomas Taimre, and Zdravko I. Botev. Handbook of monte carlo methods. Vol. 706. John Wiley & Sons, 2013.
Robert, Christian, and George Casella. Monte Carlo statistical methods. Springer Science & Business Media, 2013.
Robert, Christian P., George Casella, and George Casella. Introducing monte carlo methods with r. Vol. 18. New York: Springer, 2010.
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.
General course
Any session :
- In-person
oral exam
- Remote
written work
- If evaluation in "hybrid"
preferred remote
Additional information:
Evaluation of the course happens through the completion of an individual project.
If requested, an oral continuation of the exam is possible (can change the final grade up to 2 points, either positive or negative). The oral continuation will consist of one theoretical question and one question/clarification on the completed project.
Project complement
To be determined in terms of the project. Precise information will be communicated at the beginning of the course.
Work placement(s)
Organizational remarks
Contacts
General course
Professeur: Arnout Van Messem
Assistant: Carole Baum
Project complement
Professeur: Arnout Van Messem
Assistant: Carole Baum