 | Study programme 2017-2018 | Français | |
 | Advanced Data Science and Machine Learning | ||
Activité d'apprentissage à la Faculty of Engineering |
| Code | Lecturer(s) | Associate Lecturer(s) | Subsitute Lecturer(s) et other(s) |
|---|---|---|---|
| I-MARO-220 |
|
| Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term |
|---|---|---|---|---|---|---|---|
| Anglais | Anglais | 30 | 0 | 30 | 0 | 0 | Q2 |
Content of Learning Activity
- deep networks - reinforcement learning - active learning - ensemble methods (random forests, ...) - statistical learning theory
Required Learning Resources/Tools
- slides of oral presentations (theory and examples) - problem sets
Recommended Learning Resources/Tools
Not applicable
Other Recommended Reading
- Abu-Mostafa, Yaser S., Malik Magdon-Ismail, and Hsuan-Tien Lin. <em>Learning from data</em>. (2012) - Mitchell, Tom M. <em>Machine learning</em> (1997). - Christopher M. Bishop, <em>Pattern Recognition and Machine Learning (2012)</em>
Mode of delivery
- Face to face
Type of Teaching Activity/Activities
- Cours magistraux
- Conférences
- Projets supervisés
Evaluations
The assessment methods of the Learning Activity (AA) are specified in the course description of the corresponding Educational Component (UE)