SUPERVISED STATISTICAL LEARNING 2020/2021

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

93050 - SUPERVISED STATISTICAL LEARNING

Scheda insegnamento

SDGs