 | Study programme 2020-2021 | Français | |
| Machine learning | |||
Learning Activity |
| Code | Lecturer(s) | Associate Lecturer(s) | Subsitute Lecturer(s) et other(s) | Establishment |
|---|---|---|---|---|
| S-INFO-256 |
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| Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term |
|---|---|---|---|---|---|---|---|
| Anglais, Français | Anglais, Français | 30 | 30 | 0 | 0 | 0 | Q2 |
| Organisational online arrangements for the end of Q3 2020-2021 assessments (Covid-19) |
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Organisational arrangements for the end of Q2 2020-2021 assessments (Covid-19) online or face-to-face (according to assessment schedule)
- Written exam (multiple choice, open questions)
- Production of individual or group work, essay, report, dissertation...
Description of the modifications to the Q2 2020-2021 assessment procedures (Covid-19) online or face-to-face (according to assessment schedule)
The evaluation will be based on a written exam of 4 hours (70% of the mark) and a project (30% of the mark). The written exam will take place face-to-face if possible, otherwise it will be replaced by a test on Moodle (of the same duration).
Content of Learning Activity
The course introduces statistical and machine learning methods for predictive modelling based on big datasets. The unit covers, among other topics, linear and non-linear methods for regression, classification, clustering and dimensionality reduction.
Required Learning Resources/Tools
Not applicable
Recommended Learning Resources/Tools
Not applicable
Other Recommended Reading
Not applicable
Mode of delivery
- Mixed
Type of Teaching Activity/Activities
- Cours magistraux
- Travaux pratiques
Evaluations
The assessment methods of the Learning Activity (AA) are specified in the course description of the corresponding Educational Component (UE)