 | Study programme 2019-2020 | Français | |
| First-Order Methods for Large Scale Machine Learning | |||
Learning Activity |
| Code | Lecturer(s) | Associate Lecturer(s) | Subsitute Lecturer(s) et other(s) | Establishment |
|---|---|---|---|---|
| I-MARO-303 |
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| Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term |
|---|---|---|---|---|---|---|---|
| Anglais | Anglais | 8 | 16 | 0 | 0 | 0 | Q1 |
| Organisational online arrangements for the end of Q3 2019-2020 assessments (Covid-19) |
|---|
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| Description of the modifications to the Q3 2019-2020 online assessment procedures (Covid-19) |
| Report on the project. |
Content of Learning Activity
Organization of the calss: - Introduction and motivation to the use of firt-order methods. - Optimal first-order methods for convex optimization. - Stochastic gradient methods. - Project: Comparison of first-order methods for solving a classification problem.
Required Learning Resources/Tools
Slides and other references available on Moodle
Recommended Learning Resources/Tools
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.
Other Recommended Reading
Not applicable
Mode of delivery
- Face to face
Type of Teaching Activity/Activities
- Cours magistraux
- Travaux pratiques
- Projet sur ordinateur
Evaluations
The assessment methods of the Learning Activity (AA) are specified in the course description of the corresponding Educational Component (UE)