Study programme 2023-2024 | 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
Access to the practical sessions may be conditioned by the submission of a preparatory work.
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
Type of Teaching Activity/Activities
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
Location of assessment