 | Study programme 2020-2021 | Français | |
 | Graphs and artificial intelligence | ||
Programme component of Master's in Mathematics à la Faculty of Science |
| Students are asked to consult the ECTS course descriptions for each learning activity (AA) to know what special Covid-19 assessment methods are possibly planned for the end of Q3 |
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| Code | Type | Head of UE | Department’s contact details | Teacher(s) |
|---|---|---|---|---|
| US-M1-SCMATH-054-M | Compulsory UE | MELOT Hadrien | S825 - Algorithmique |
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| 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 |
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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
The goal of this course is that students deepen some classical topics of Artificial Intelligence. The students will be able to identify when a particular method can be applied. The course will concentrate on algorithmic aspect of Artificial Intelligence.
Content of UE
See unique learning activity.
Prior Experience
Follow with success the course "Intelligence Artificielle".
Type of Assessment for UE in Q1
- Oral examination
Q1 UE Assessment Comments
Oral exam 100%
Type of Assessment for UE in Q2
- Oral Examination
Q2 UE Assessment Comments
Oral exam 100%
Type of Assessment for UE in Q3
- Oral examination
Q3 UE Assessment Comments
Oral exam 100%
Type of Resit Assessment for UE in Q1 (BAB1)
- N/A
Q1 UE Resit Assessment Comments (BAB1)
Not applicable.
Type of Teaching Activity/Activities
| AA | Type of Teaching Activity/Activities |
|---|---|
| S-INFO-021 |
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Mode of delivery
| AA | Mode of delivery |
|---|---|
| S-INFO-021 |
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Required Reading
| AA | |
|---|---|
| S-INFO-021 |
Required Learning Resources/Tools
| AA | Required Learning Resources/Tools |
|---|---|
| S-INFO-021 | Not applicable |
Recommended Reading
| AA | |
|---|---|
| S-INFO-021 |
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 | Authorized |