- 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)
Learning outcomes
By the end of the course the student gains an understanding of theory and computing of modern statistical methods, with particular emphasis on methods for analysing large amounts of data (big data). More specifically, the student acquires knowledge on the most important methods of statistical learning and prediction and the skills required to solve real-world and decision-making problems.
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