93050 - Supervised Statistical Learning

Learning outcomes

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.

Course contents

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
  

Readings/Bibliography

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

Teaching methods

Lectures and practical sessions.

Assessment methods

Written exam with theoretical questions and practical exercises to be solved in R.

Teaching tools

The following material will be provided: slides of the lectures, exercises with solutions, mock exam.

Office hours

See the website of Laura Anderlucci