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 :
Project complement :
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)
General course
face-to-face
Project complement
face-to-face
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
General course
To be determined in terms of the project. Precise information will be communicated at the beginning of the course.
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
Yvik Swan
Office : B37 0/68
Phone : +32 4 366 94 76
Email : yswan at ulg.ac.be
Project complement
Yvik Swan
Office : B37 0/68
Phone : +32 4 366 94 76
Email : yswan at ulg.ac.be
Adaptation of teaching commitments following the COVID-19 pandemic for the May-June 2020 session
Teaching methods implemented : distance-learning
General course
eCampus
Project complement
eCampus
Assessment subjects
General course
Monte Carlo methods, Models and challenges, Generating random variables
Project complement
Monte Carlo methods, Models and challenges, Generating random variables.
Assessment methods
General course
Project
Project complement
Project
Contacts
General course
Amir Aboubacar
a.aboubacar@uliege.be
Project complement
Amir Aboubacar
a.aboubacar@uliege.be
Adaptation of teaching commitments following the COVID-19 pandemic for the Aug-Sept 2020 session
Assessment subjects
General course
Monte Carlo methods, Models and challenges, Generating random variables.
Assessment methods
General course
Project
Contacts
General course
Amir Aboubacar
a.aboubacar@uliege.be