- Docente: Christian Martin Hennig
- Crediti formativi: 6
- SSD: SECS-S/01
- Lingua di insegnamento: Inglese
- Modalità didattica: Convenzionale - Lezioni in presenza
- Campus: Bologna
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Corso:
Laurea Magistrale in
Statistical sciences (cod. 9222)
Valido anche per Laurea Magistrale in Statistical sciences (cod. 9222)
Conoscenze e abilità da conseguire
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.
Contenuti
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
Testi/Bibliografia
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
Metodi didattici
Classroom lessons, tutorials, computer workshop
Modalità di verifica e valutazione dell'apprendimento
2 hours written exam. 5/30 marks can be earned from homework activity.
Strumenti a supporto della didattica
Lecture Notes, supporting material provided on the web
Orario di ricevimento
Consulta il sito web di Christian Martin Hennig