 | Study programme 2021-2022 | Français | |
| Artificial Intelligence and graphs | |||
Learning Activity |
| Code | Lecturer(s) | Associate Lecturer(s) | Subsitute Lecturer(s) et other(s) | Establishment |
|---|---|---|---|---|
| S-INFO-021 |
|
|
| Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term |
|---|---|---|---|---|---|---|---|
| Français | Français | 20 | 10 | 0 | 0 | 0 | Q1 |
Content of Learning Activity
Since this course is a followup of the course "Artificial Intelligence", it covers some topics of AI allowing to complete and deepen the topics previously covered, as CSP, probabilistic reasoning, robotics, approximation algorithms, computer-assisted discovery in graph theory, etc. Remark: this AA does not cover machine or deep learning since these topics are already covered in other specific courses.
Required Learning Resources/Tools
Not applicable
Recommended Learning Resources/Tools
Not applicable
Other Recommended Reading
Russel, S. and Norvig, P., Artificial Intelligence: A Modern Approach, 3ième édition, Pearson, 2010 Williamson, Shmoys, The Design of Approximation Algorithms, Cambridge University Press (2011). Electronic version available online: www.designofapproxalgs.com
Mode of delivery
- Face to face
Type of Teaching Activity/Activities
- Cours magistraux
- Exercices dirigés
Evaluations
The assessment methods of the Learning Activity (AA) are specified in the course description of the corresponding Educational Component (UE)