Introduction to Machine Learning


Teacher: Alberto Bemporad

Objectives

The goal of the course is to provide a concise introduction to the most popular and practical techniques for learning mathematical models from data. Different methods for solving function regression, classification, and clustering problems will be illustrated, understanding their mathematical foundations, the underlying learning algorithms, and how to validate their prediction performance. Examples from different application domains and how to solve them in Python will be shown during the course to illustrate the concepts.

Syllabus

Introduction to machine learning: supervised/unsupervised learning, classification/regression, overfitting, bias/variance tradeoff, cross-validation, examples. Linear regression and least squares: loss functions and regularization, basis functions and Kernel least squares, support vector regression, recursive least squares. Linear and Bayesian classification: ridge classifier, logistic regression, support vector classification, naïve Bayes classifier. Non-parametric regression and classification: nearest neighbors, decision trees, Gaussian process regression, ensemble methods (bagging, bootstrap, random forests and feature importance, boosting methods). Neural networks: feedforward networks, backpropagation and automatic differentiation, learning algorithms (stochastic gradient descent, nonlinear least squares), AutoML; temporal convolutional networks, recurrent neural networks. Unsupervised learning: clustering methods (K-means clustering, density-based spatial clustering), dimensionality reduction (principal component analysis, nonlinear PCA), autoencoders.

Prerequisites

Basics of calculus, linear algebra, numerical optimization, probability theory, computer programming.

Timetable

Monday January 19, 2026 09.00-11.00
Wednesday January 21, 2026 09.00-11.00
Friday January 23, 2026 09.00-11.00
Monday January 26, 2026 09.00-11.00
Wednesday January 28, 2024 09.00-11.00
Friday January 30, 2026 09.00-11.00
Monday February 2, 2026 09.00-11.00
Wednesday February 4, 2026 09.00-11.00
Friday February 6, 2026 09.00-11.00
Wednesday February 11, 2026 09.00-11.00
Friday February 13, 2026 09.00-11.00
Monday February 16, 2026 09.00-11.00

Location

IMT School for Advanced Studies Lucca

Lecture slides

Introduction to machine learning
Linear models for regression
Classification: linear models, naïve Bayes
Nonparametric models for classification and regression
Neural networks
Unsupervised learning (clustering, dimensionality reduction)