Duration
24h Th, 12h Pr, 40h Proj.
Number of credits
| Master of Science (MSc) in Data Science | 5 crédits | |||
| Master of Science (MSc) in Data Science and Engineering | 5 crédits | |||
| Master in mathematics (120 ECTS) | 8 crédits |
Lecturer
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 covers (a selection of) the following topics:
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.
A good understanding of the problematics related to simulation and sampling.
Prerequisite knowledge and skills
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
The course is offered through online videos which the student can watch at his/her own pace. It consists of theory sessions as well as practical sessions (both written and on computer). Q&A sessions will be organized regularly.
Mode of delivery (face to face, distance learning, hybrid learning)
Blended learning
Additional information:
Classes will be given through online videos.
Q&A sessions will be organized regularly. Details will be given on the eCampus platform.
Recommended or required readings
All information (course notes, project and exercise sheets, videos) will be made available through 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.
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)
Organisational remarks and main changes to the course
Contacts
Professeur: Arnout Van Messem