![]() | Study programme 2024-2025 | Français | |
![]() | Advanced Optimization for Data Science | ||
Programme component of Master's in Computer Engineering and Management : Specialist Focus on Artificial Intelligence and Decision Aid (MONS) (day schedule) à la Faculty of Engineering |
Code | Type | Head of UE | Department’s contact details | Teacher(s) |
---|---|---|---|---|
UI-M1-IRIGIA-103-M | Compulsory UE | VANDAELE Arnaud | F151 - Mathématique et Recherche opérationnelle |
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Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Credits | Weighting | Term |
---|---|---|---|---|---|---|---|---|---|
| Anglais | 26 | 34 | 0 | 0 | 0 | 5 | 5.00 | 2nd term |
AA Code | Teaching Activity (AA) | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term | Weighting |
---|---|---|---|---|---|---|---|---|
I-MARO-232 | Topics in Convex Optimization | 8 | 16 | 0 | 0 | 0 | Q2 | |
I-MARO-303 | First-Order Methods for Large Scale Machine Learning | 6 | 12 | 0 | 0 | 0 | Q2 | |
I-MARO-018 | Optimization & Operational Research | 12 | 6 | 0 | 0 | 0 | Q2 |
Programme component |
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Objectives of Programme's Learning Outcomes
Learning Outcomes of UE
see the different AA
UE Content: description and pedagogical relevance
Global mark.
A minimum score to be achieved in each of the AAs will be communicated.
Prior Experience
Numerical Analysis, optimization and programming skills
Type(s) and mode(s) of Q2 UE assessment
Q2 UE Assessment Comments
Global mark.
A minimum score to be achieved in each of the AAs will be communicated.
Method of calculating the overall mark for the Q2 UE assessment
Global mark.
A minimum score to be achieved in each of the AAs will be communicated.
Type(s) and mode(s) of Q3 UE assessment
Q3 UE Assessment Comments
Global mark.
A minimum score to be achieved in each of the AAs will be communicated.
Method of calculating the overall mark for the Q3 UE assessment
Global mark.
A minimum score to be achieved in each of the AAs will be communicated.
Type of Teaching Activity/Activities
AA | Type of Teaching Activity/Activities |
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I-MARO-232 |
|
I-MARO-303 |
|
I-MARO-018 |
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Mode of delivery
AA | Mode of delivery |
---|---|
I-MARO-232 |
|
I-MARO-303 |
|
I-MARO-018 |
|
Required Learning Resources/Tools
AA | Required Learning Resources/Tools |
---|---|
I-MARO-232 | Not applicable |
I-MARO-303 | Slides and other references available on Moodle |
I-MARO-018 | Not applicable |
Recommended Learning Resources/Tools
AA | Recommended Learning Resources/Tools |
---|---|
I-MARO-232 | Not applicable |
I-MARO-303 | Bottou, L., Curtis, F. E., & Nocedal, J. (2018). Optimization methods for large-scale machine learning. Siam Review, 60(2), 223-311. Newton, D., Yousefian, F., & Pasupathy, R. (2018). Stochastic Gradient Descent: Recent Trends. In Recent Advances in Optimization and Modeling of Contemporary Problems (pp. 193-220). INFORMS. |
I-MARO-018 | Not applicable |
Other Recommended Reading
AA | Other Recommended Reading |
---|---|
I-MARO-232 | Not applicable |
I-MARO-303 | Not applicable |
I-MARO-018 | Not applicable |