Scheda insegnamento
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Docente Laura Anderlucci
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Crediti formativi 6
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SSD SECS-S/01
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Modalità didattica Convenzionale - Lezioni in presenza
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Lingua di insegnamento Inglese
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Campus di Bologna
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Corso Laurea Magistrale in Statistical sciences (cod. 9222)
SDGs
L'insegnamento contribuisce al perseguimento degli Obiettivi di Sviluppo Sostenibile dell'Agenda 2030 dell'ONU.

Anno Accademico 2020/2021
Conoscenze e abilità da conseguire
By the end of the course the student knows the fundamentals of the most important multivariate techniques to build supervised statistical models for predicting or estimating an output based on one or more inputs. The student is able to represent and organize knowledge about large-scale data collections, and to turn data into actionable knowledge.
Contenuti
Part 0: Introduction to Supervised Statistical Learning
Part 1: Resampling methods
- Cross-Validation
Part 2: Classification
- Naive Bayes
- k-Nearest Neighbours
- Logistic Regression
- Linear Discriminant Analysis
Part 3: Dimension Reduction and Regularisation
Part 4: Tree-based methods
- Regression and Classification trees
- Bagging; Random Forests; Boosting
Part 5: Overview of the main machine learning methods
- Support Vector Machines
- Neural Networks
Testi/Bibliografia
The primary text for the course:
- James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to Statistical Learning. New York: Springer.
Freely available at: http://www-bcf.usc.edu/~gareth/ISL/ISLR%20Seventh%20Printing.pdf
In addition, we will use:
- T. Hastie, R. Tibshirani, and J. Friedman (2001) The Elements of Statistical Learning: data mining, inference and prediction. Springer Verlag.
Freely available at: https://web.stanford.edu/~hastie/Papers/ESLII.pdf
Metodi didattici
Lectures and practical sessions.
Modalità di verifica e valutazione dell'apprendimento
Written exam with theoretical questions and practical exercises to be solved in R.
Strumenti a supporto della didattica
The following material will be provided: slides of the lectures, exercises with solutions, mock exam.
Orario di ricevimento
Consulta il sito web di Laura Anderlucci