 | Study programme 2022-2023 | Français | |
| Models and methods in Data Sciences | |||
Learning Activity |
| Code | Lecturer(s) | Associate Lecturer(s) | Subsitute Lecturer(s) et other(s) | Establishment |
|---|---|---|---|---|
| I-MARO-015 |
|
|
| Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term |
|---|---|---|---|---|---|---|---|
| Anglais | Anglais, Français | 12 | 12 | 0 | 0 | 0 | Q2 |
Content of Learning Activity
In this class, we analyze different stochastic models from operational reasearch. In particular, we focus on Markov chains and their applications (PageRank of Google, Queing systems, etc.). We will also study matrix factorization in the context of unsupervised learning, and also RSA encryption.
Required Learning Resources/Tools
Slides
Recommended Learning Resources/Tools
Sans objet
Other Recommended Reading
Not applicable
Mode of delivery
- Face-to-face
Type of Teaching Activity/Activities
- Cours magistraux
- Conférences
- Exercices dirigés
- Utilisation de logiciels
- Démonstrations
Evaluations
The assessment methods of the Learning Activity (AA) are specified in the course description of the corresponding Educational Component (UE)
(*) HT : Hours of theory - HTPE : Hours of in-class exercices - HTPS : hours of practical work - HD : HMiscellaneous time - HR : Hours of remedial classes. - Per. (Period), Y=Year, Q1=1st term et Q2=2nd term