Course overview
This is the homepage for MATH-517 Statistical computation and visualisation in Fall 2025 at EPFL. All course materials will be posted on this site.
You can find the course syllabus here and the course schedule here. All the additional ressources can be found either on this page or by using the search bar on the top left corner of the website. These ressources include supplementary material, tutorials, tips and tricks, etc…
1 Course Organization
| Meeting | Location | Time |
|---|---|---|
| Lectures | GCD 03 86 | Fri 10:15 - 12:00 |
| Exercises | CM 1 221 | Fri 13:15 - 15:00 |
2 Course Content
As far as the lecture topics go, the first 2 lectures will be less mathematical, while the remaining lectures will be somehow more classical. However, the focus of this course is on methods (why and how they work) and algorithms, not on inference or modeling.
Apart from the topics mentioned in the schedule, mastering this course requires scripting (simulation studies and visualization tasks), as well as sharing your code via GitHub. You will be required to do a project, and various assignments fostering these skills. Exercise classes should be attended and used to pick up the required skills, and work on the assignments and project! Additional exercises can be found in the Exercices tab on this website.
3 Evaluation
The assessment method for this course is “contrôle continue”, meaning that the course and all the work required from the students effectively ends before Christmas. This also means that the work needed to pass this course starts immediately on Week 1.
The final grade will consist of
- handing in assignments – 60 % of the grade, handed-in individually
- 8 assignments in total, the first for 4 % of the grade and the remaining 7, each for 8% of the grade (4% for the theoretical part + 4% for the computational part)
- collaboration during the exercise classes is encouraged, but avoid copies
- final project – 40 % of the grade, handed-in in groups of 2-3 student. 1
Grading will be done on a rough scale. For example, each part of the assignment (theoretical or computational) will be mostly graded on a three-point scale, you either receive full 4% (1 point) or 2% (0.5 points) or nothing (0 points). The project will be evaluated based on the following criteria: Exposition (clarity, structure, and logic of the writing and presentation), involvement (evidence of active engagement and personal effort), comprehensiveness (how thoroughly and appropriately the problem was addressed), and mastery (understanding and application of relevant statistical methods).
Deadlines will always be set at the end of the week (the midnight between Sunday and Monday), hence, e.g., “deadline on Week 2” simply means the assignment can be handed-in by 23:59 on September 21. An assignment from Week \(k\) (given at the end of slides to the \(k\)-th lecture) will have their deadline on Week \(k+1\) (i.e., the midnight between Sunday and Monday following the \((k+1)\)-th lecture, see the schedule). Some exceptions might apply.
3.1 Assignments
There will be 8 assignments during the semester, graded as described above. Assignments can be found in the Assignments tab on this website (or equivalently in the schedule). Exercises are not mandatory, only recommended. Note that the first assignment will only have a computational part (setting up the GitHub machinery) while the remaining assignments will have a theoretical part and a computational part.
To accept the assignment, you need to first access the google document with all the invite links, then click on the link corresponding to the assignment you want to accept. This will create a repository for you on GitHub. You can then clone this repository on your computer and start working on the assignment.
3.2 Project
The main project can start following the \(7\)-th lecture, deadline on December 21, at 23:59. This is a soft deadline. I would suggest you finish the project before Christmas, however, if all members of the team agree to this, the project can be submitted by the end of the calendar year. This is recommended in order to prevent the holiday season ruined by a lazy member(s) of the team. Note that if a single member of your team wishes to submit on December 21, you are required to do so. See here for details.
Groups can be of size of either 2 or 3 people. The size will not matter w.r.t. to grading. However, a group of size 3 will have one additional task to do: as part of their submission, every team member will individually include a short paragraph describing contributions of every individual member of the team. This is not to be discussed among the team members, as it serves as a safeguard. Regardless of their individual contributions, each member of the team will receive the same grade, apart from where this would be extremely unfair. Such cases will be discussed personally. In case of any team-work problems, the students are encouraged to seek advice (mostly as a group) from the teachers (mostly during the exercise classes).
4 Materials
All materials will be gradually made available on this website. Assignments and exercices can be accessed through their respective tabs, supplemental materials can be found in the Ressources tab. Lecture slides will be posted in the schedule.
5 Handing-in Assignments and Project
We will use GitHub Classroom to handle the assignments and the project for this course. See this page for more information on how to use GitHub Classroom.
5.1 License
This online work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International. Visit here for more information about the license.
This website and part of the course materials where adapted from different sources:
Footnotes
Details on how group submissions will be handled will be provided later in the semester.↩︎