- Docente: Christian Martin Hennig
- Credits: 6
- SSD: SECS-S/01
- Language: English
- Teaching Mode: Traditional lectures
- Campus: Bologna
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Corso:
Second cycle degree programme (LM) in
Statistical Sciences (cod. 9222)
Also valid for Second cycle degree programme (LM) in Statistical Sciences (cod. 9222)
Course contents
Cluster analysis: k-means, construction of distances, hierarchical clustering, partitioning around medoids, average silhouette width, mixture models, with algorithms, R-coding, theory, applications and in-depth discussion
Dimension reduction: Variable and feature selection in regression, cross-validation, model selection criteria, Lasso, with algorithms, R-coding, theory, applications and in-depth discussion
Readings/Bibliography
Everitt, B. S., Landau, S., Leese, M., Cluster Analysis (fourth edition), E. Arnold 2001
Hennig, C., Meila, M., Murtagh, F., and Rocci, R., Handbook of Cluster Analysis, Taylor & Francis 2016.
Hastie, T., Tibshirani, R., Friedman, J., The Elements of Statistical Learning (second edition), Springer 2009.
Lecture Notes
Teaching methods
Classroom lessons, tutorials, computer workshop
Assessment methods
2 hours written exam. 5/30 marks can be earned from homework activity.
Teaching tools
Lecture Notes, supporting material provided on the web
Office hours
See the website of Christian Martin Hennig