The aim of the unit is to study different probability models and specific algorithmic methods for data learning and to apply them with R software
| On completion of the unit, the student will be capable of: | Classification level | Priority |
|---|---|---|
| Understanding the different statistical models of supervised and non-supervised learning and the validation methods of these models | 2. Understand | Essential |
| Knowing how to recognize the nature of the set problem and to apply the appropriate models | 3. Apply | Essential |
| Analysing and of criticising obtained results | 4. Analyse | Essential |
| Producing a professional report, summarising the obtained results | 6. Assess | Important |
| Percentage ratio of individual assessment | Percentage ratio of group assessment | ||||
|---|---|---|---|---|---|
| Written exam: | 60 | % | Project submission: | 0 | % |
| Individual oral exam: | 0 | % | Group presentation: | 0 | % |
| Individual presentation: | 0 | % | Group practical exercise: | 0 | % |
| Individual practical exercise: | 40 | % | Group report: | 0 | % |
| Individual report: | 0 | % | |||
| Other(s): 0 % | |||||
| Type of teaching activity | Content, sequencing and organisation |
|---|---|
| Course | Generalities: bias- variance dilemma, model validation |
| Course | Non-supervised learning (in addition to the Exploratory statistics and mathematical tools unit): association rules In addition to the theoretical course a practical case study will be undertaken. |
| Course | Supervised learning of approximation: linear methods (regressions), neurone networks, trees, etc. Application to a case study. |
| Course | Supervised learning of classification: linear methods (discriminatory analysis, logistics regression), closest neighbour methods, support vector machines (SVM). Application to a case study |