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
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
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
This course contributes to the learning outcomes I.1, I.2, I.3, II.1, IV.4, VI.1, VII.2, VII.4 of the MSc in data science and engineering.
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. The precise modalities will be announced during the first class.
Project complement
Ex cathedra teaching, exercise sessions (both on computer and on paper). An end of term assignement is planned. The precise modalities will be announced during the first class.
Mode of delivery (face to face, distance learning, hybrid learning)
General course
Blended learning
Additional information:
In consultation with the students, the course can be organised either fully face-to-face, or in a hybrid version where the theory will delivered through online videos and regular (offline) Q&A sessions will be organised.
Project complement
Blended learning
Additional information:
Blended learning
Additional information:
In consultation with the students, the course can be organised either fully face-to-face, or in a hybrid version where the theory will delivered through online videos and regular (offline) Q&A sessions will be organised.
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.
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.
Assessment methods and criteria
General course
Exam(s) in session
Any session
- In-person
oral exam
Written work / report
Additional information:
The evaluation of the course happens through the completion of an individual project.
If desired, an oral continuation of the exam is possible. The oral examination 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
Exam(s) in session
Any session
- In-person
oral exam
Written work / report
Additional information:
Exam(s) in session
Any session
- In-person
oral exam
Written work / report
Additional information:
The evaluation of the course happens through the completion of an individual project.
If desired, an oral continuation of the exam is possible. The oral examination 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.
Work placement(s)
Organizational remarks
Contacts
General course
Professeur: Arnout Van Messem
Assistant: Carole Baum
Project complement
Professeur: Arnout Van Messem
Assistant: Carole Baum