 | Study programme 2022-2023 | Français | |
 | Graphs and artificial intelligence | ||
Programme component of Master's in Mathematics (MONS) (day schedule) à la Faculty of Science |
| Code | Type | Head of UE | Department’s contact details | Teacher(s) |
|---|---|---|---|---|
| US-M1-SCMATH-054-M | Compulsory UE | MELOT Hadrien | S825 - Algorithmique |
|
| Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Credits | Weighting | Term |
|---|---|---|---|---|---|---|---|---|---|
| Français | 20 | 10 | 0 | 0 | 0 | 5 | 5.00 | 1st term |
| AA Code | Teaching Activity (AA) | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term | Weighting |
|---|---|---|---|---|---|---|---|---|
| S-INFO-021 | Artificial Intelligence and graphs | 20 | 10 | 0 | 0 | 0 | Q1 | 100.00% |
| Programme component |
|---|
Objectives of Programme's Learning Outcomes
- Have integrated and elaborate mathematical knowledge.
- Mobilise the Bachelor's course in mathematics to address complex issues and have profound mathematical expertise to complement the knowledge developed in the Bachelor's course.
- Use prior knowledge to independently learn high-level mathematics.
- Apply innovative methods to solve an unprecedented problem in mathematics or within its applications.
- Appropriately make use of computer tools, as required by developing a small programme.
Learning Outcomes of UE
At the end of this learning activity, students will have deepened certain areas linking graph theory and Artificial Intelligence.
UE Content: description and pedagogical relevance
See unique learning activity.
Prior Experience
Follow with success the course "Intelligence Artificielle".
Type of Teaching Activity/Activities
| AA | Type of Teaching Activity/Activities |
|---|---|
| S-INFO-021 |
|
Mode of delivery
| AA | Mode of delivery |
|---|---|
| S-INFO-021 |
|
Required Learning Resources/Tools
| AA | Required Learning Resources/Tools |
|---|---|
| S-INFO-021 | Not applicable |
Recommended Learning Resources/Tools
| AA | Recommended Learning Resources/Tools |
|---|---|
| S-INFO-021 | Not applicable |
Other Recommended Reading
| AA | Other Recommended Reading |
|---|---|
| S-INFO-021 | 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 |
Grade Deferrals of AAs from one year to the next
| AA | Grade Deferrals of AAs from one year to the next |
|---|---|
| S-INFO-021 | Unauthorized |
Term 1 Assessment - type
| AA | Type(s) and mode(s) of Q1 assessment |
|---|---|
| S-INFO-021 |
|
Term 1 Assessment - comments
| AA | Term 1 Assessment - comments |
|---|---|
| S-INFO-021 | Oral exam 100% |
Resit Assessment - Term 1 (B1BA1) - type
| AA | Type(s) and mode(s) of Q1 resit assessment (BAB1) |
|---|---|
| S-INFO-021 |
|
Term 3 Assessment - type
| AA | Type(s) and mode(s) of Q3 assessment |
|---|---|
| S-INFO-021 |
|
Term 3 Assessment - comments
| AA | Term 3 Assessment - comments |
|---|---|
| S-INFO-021 | Oral examination 100% |
(*) 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