 | Study programme 2024-2025 | Français | |
| Advanced Deep Learning | |||
Learning Activity |
| Code | Lecturer(s) | Associate Lecturer(s) | Subsitute Lecturer(s) et other(s) | Establishment |
|---|---|---|---|---|
| I-ILIA-202 |
|
|
| Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term |
|---|---|---|---|---|---|---|---|
| Anglais, Français | Anglais, Français | 6 | 6 | 0 | 0 | 0 | Q1 |
Content of Learning Activity
- Recall : basics of deep neural networks
- Analysis and visulisation of energy data (tempral series, numercial values, etc.) ;
- Implemenation and exploitation of new neural architectures (CNN, RNN, LSTM, Transformers, etc.) ;
- Implementation of the techniques of performance evaluation, interpretation and visualization of deep learning results ;
- Performance optimization: setting hyper parameters, regularization, batch normalization, cross validation, etc.
- Explainable Deep Learning.
Required Learning Resources/Tools
Not applicable
Recommended Learning Resources/Tools
Not applicable
Other Recommended Reading
Not applicable
Mode of delivery
- Face-to-face
Type of Teaching Activity/Activities
- Cours magistraux
- Travaux pratiques
- Projet sur ordinateur
- Etudes de cas
Evaluations
The assessment methods of the Learning Activity (AA) are specified in the course description of the corresponding Educational Component (UE)
Location of learning activity
- Université de Mons - Mons
Location of assessment
- Université de Mons - Mons