Content of Learning Activity

The pedagogical material is available online: https://wp.sciviews.org. The chapters of this AA are:

- Classification I - LDA, general principle, confusion matrice, metrics
- Classification II - corss-validation, AUC, k-nn, lvq, raport, random forest
- Classification III = svm, neural networks, initiation to deep learning
- Time series I - description, manipulation, acf, spectral analysis
- Time series II - decomposition & regularisation
- Spatial statistics, initiation, maps & krigging

Required Learning Resources/Tools

The content for this course is available online https://wp.sciviews.org

Recommended Learning Resources/Tools

 Not applicable

Other Recommended Reading

Barnier, J., 2018. Introduction à R et au tidyverse (https://juba.github.io/tidyverse/index.html). Ismay, Ch. & Kim A.Y, 2018. Moderndive: An introduction to statistical and data science via R (http://moderndive.com). Wickham, H. & Grolemund, G, 2017. R for data science (http://r4ds.had.co.nz). Zar, J.H., 2010. Biostatistical analysis (5th ed.). Pearson Education, London. 944pp. Dagnelie, P., 2007. Statistique théorique et appliquée, Volumes I et II (2ème ed.). De Boeck & Larcier, Bruxelles. 511pp (vol. I) 734pp (vol. II). Venables W.N. & B.D. Ripley, 2002. Modern applied statistics with S-PLUS (4th ed.). Springer, New York, 495 pp. Legendre, P. & L. Legendre, 1998. Numerical ecology (2nd ed.). Springer Verlag, New York. 587 pp.

Mode of delivery

• Hybrid

Type of Teaching Activity/Activities

• Conférences
• Travaux pratiques
• Exercices de création et recherche en atelier
• 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)