 | Study programme 2022-2023 | Français | |
| Introduction to Digital Intelligence | |||
Learning Activity |
| Code | Lecturer(s) | Associate Lecturer(s) | Subsitute Lecturer(s) et other(s) | Establishment |
|---|---|---|---|---|
| I-ISIA-021 |
|
|
| Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term |
|---|---|---|---|---|---|---|---|
| Français | Français | 16 | 20 | 0 | 0 | 0 | Q1 |
Content of Learning Activity
Digital information coding
Information analysis (clustering, principal componenent analysis)
Classification (Bayes theory, Gaussian Mixture Models)
Dynamic systems (dynamic time warping, Hidden Markov Models)
Introduction to Artificial Neural Networks and Deep Learning
Teaching modalities may be adjusted according to the teaching context imposed by health measures.
Required Learning Resources/Tools
Copies of presentations
Laboratory protocols
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)