 | Study programme 2022-2023 | Français | |
 | Optimisation | ||
Programme component of Master's in Computer Science (MONS) (day schedule) à la Faculty of Science |
| Code | Type | Head of UE | Department’s contact details | Teacher(s) |
|---|---|---|---|---|
| US-MC-SCINFO-042-M | Compulsory UE | GILLIS Nicolas | F151 - Mathématique et Recherche opérationnelle |
|
| Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Credits | Weighting | Term |
|---|---|---|---|---|---|---|---|---|---|
| Français | 14 | 14 | 0 | 0 | 0 | 3 | 3.00 | 1st term |
| AA Code | Teaching Activity (AA) | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term | Weighting |
|---|---|---|---|---|---|---|---|---|
| I-MARO-035 | Linear Optimization | 14 | 14 | 0 | 0 | 0 | Q1 | 100.00% |
| Programme component |
|---|
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.
- Manage research, development and innovation.
- Understand unprecedented problems in computer science and its applications.
- Organise and lead a research, development or innovation project to completion.
- Methodically research valid scientific information, lead a critical analysis, propose and argue potentially innovative solutions to targeted problems.
- Apply scientific methodology.
- Critically reflect on the impact of IT in general, and on the contribution to projects.
- Demonstrate thoroughness, independence, creativity, intellectual honesty, and ethical values.
Learning Outcomes of UE
- Model an optimization problem- Choose a suitable method to solve an optimization problem- Develop and apply optimization methods
UE Content: description and pedagogical relevance
This class analyzes models and methods for linear optimization problems (continuous and discrete).
Prior Experience
Not applicable
Type of Teaching Activity/Activities
| AA | Type of Teaching Activity/Activities |
|---|---|
| I-MARO-035 |
|
Mode of delivery
| AA | Mode of delivery |
|---|---|
| I-MARO-035 |
|
Required Learning Resources/Tools
| AA | Required Learning Resources/Tools |
|---|---|
| I-MARO-035 | Linear algebra |
Recommended Learning Resources/Tools
| AA | Recommended Learning Resources/Tools |
|---|---|
| I-MARO-035 | Slides |
Other Recommended Reading
| AA | Other Recommended Reading |
|---|---|
| I-MARO-035 | Not applicable |
Grade Deferrals of AAs from one year to the next
| AA | Grade Deferrals of AAs from one year to the next |
|---|---|
| I-MARO-035 | Authorized |
Term 1 Assessment - type
| AA | Type(s) and mode(s) of Q1 assessment |
|---|---|
| I-MARO-035 |
|
Term 1 Assessment - comments
| AA | Term 1 Assessment - comments |
|---|---|
| I-MARO-035 | 1 written Examen, 100% within the AA, duration: 2h. |
Resit Assessment - Term 1 (B1BA1) - type
| AA | Type(s) and mode(s) of Q1 resit assessment (BAB1) |
|---|---|
| I-MARO-035 |
|
Term 3 Assessment - type
| AA | Type(s) and mode(s) of Q3 assessment |
|---|---|
| I-MARO-035 |
|
Term 3 Assessment - comments
| AA | Term 3 Assessment - comments |
|---|---|
| I-MARO-035 | 1 written Examen, 100% within the AA, duration: 2h. |
(*) 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