 | Study programme | Français | |
 | Approximation Algorithms | ||
Programme component of Master's Degree in Computer Science à la Faculty of Science |
| Code | Type | Head of UE | Department’s contact details | Teacher(s) |
|---|---|---|---|---|
| US-M1-SCINFO-002-M | Compulsory UE | MELOT Hadrien | S825 - Algorithmique |
|
| Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Credits | Weighting | Term |
|---|---|---|---|---|---|---|---|---|---|
| Français | 30 | 15 | 0 | 0 | 0 | 4.00 | 100.00 |
| AA Code | Teaching Activity (AA) | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term | Weighting |
|---|---|---|---|---|---|---|---|---|
| S-INFO-021 | Approximation Algorithms | 30 | 15 | 0 | 0 | 0 | Q2 | 100.00% |
| Unité d'enseignement |
|---|
Objectives of Programme's Learning Outcomes
- Have acquired highly specialised and integrated knowledge and broad skills in the various disciplines of computer science, which come after those within the Bachelor's in computer science.
Learning Outcomes of UE
At the end of the course, the students will be initiated to the design and performance analysis of basic approximation algorithms for NP-hard optimization problems.
Content of UE
See unique learning activity.
Prior Experience
Knowledge in complexity theory, for instance, course "Calculabilité et Complexité".
Type of Assessment for UE in Q1
- N/A
Q1 UE Assessment Comments
Not applicable
Type of Assessment for UE in Q2
- Oral Examination
Q2 UE Assessment Comments
Oral examination 100% (theory 70%, exercices 30%)
Type of Assessment for UE in Q3
- Oral examination
Q3 UE Assessment Comments
Oral examination 100% (theory 70%, exercices 30%)
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 |
|
Mode of delivery
| AA | Mode of delivery |
|---|---|
| S-INFO-021 |
|
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 | - Williamson, Shmoys, The Design of Approximation Algorithms, Cambridge University Press (2011). Electronic version available online: www.designofapproxalgs.com - Ausiello, Crescenzi, Gambozi, Kann, Marchetti-Spaccamela, Protasi, Complexity and Approximation: Combinatorial Optimization Problems and Their Approximability Properties, Springer (1999) - Vazirani, Approximation Algorithms, Springer (2001) |
Grade Deferrals of AAs from one year to the next
| AA | Grade Deferrals of AAs from one year to the next |
|---|---|
| S-INFO-021 | Autorisé |