| Lecturer: | G. Garrigos |
| Period: |
Term 1 |
| ECTS: | 3 |
| Schedule | 24h |
Data science and statistics for industry
| Lecturers: | M. Abdel-Sayed & L. Massoulard |
| Period: |
Term 2 |
| ECTS: | 3 |
| Schedule | 3 hours of lectures/Tutorials per week |
Modern approach for dimension reduction
Lecturer: A. Célisse
Period: Term 3
ECTS: 6
Schedule: 2h lecture + 2h Tutorials per week
Deep learning
| Lecturer: | I. Giulini |
| Period: |
Term 3 |
| ECTS: | 3 |
| Schedule: | See Timetable |
Présentation
L’objectif du cours est de présenter les concepts fondamentaux du deep learning jusqu’aux architectures plus avancées telles que les Transformers et les GANs.
Le cours prévoit 4 séances de TP.
Programme
· Introduction to Deep Learning
· Feed-Forward Neural Networks
· Convolutional Neural Networks for image classification
· Network architectures for object detection and image segmentation
· Recurrent Neural Networks, LSTM units, GRUs
· Natural Language Processing
· Neural Machine Translation, Attention models, the Transformer Network
· Deep Generative Modeling (VAE, GAN)
Bibliographie
- Goodfellow, I. and Bengio, Y., and Courville, A. (2016). Deep Learning. MIT Press.
- Chollet, F. and Allaire, J. J. (2018). Deep Learning with R. Manning Pub.
- Chollet, F. and Allaire, J. J. (2018). Deep Learning with Python. Manning Pub.
Statistics in high dimension
Lecturer: B-E. Chérief Abdellatif
Period: Term 3
ECTS: 3
Schedule: 2h lectures per week
