ECO0278 - SEMINAR IN MACHINE LEARNING AND BIG DATA ANALYSIS

courses
ID:
ECO0278
Duration (hours):
40
CFU:
6
SSD:
ECONOMIA APPLICATA
Year:
2025

Overview

Date/time interval

Secondo Semestre (16/02/2026 - 29/05/2026)

Syllabus

Course Objectives

Machine learning (ML) is a branch of Artificial Intelligence (AI) that was originally developed to enable computers to emulate human cognition and learn from training examples to predict future events. Today, ML techniques include a number of advanced statistical methods for regression and classification applied in a wide variety of fields (including medical diagnostics, credit card fraud detection, face and speech recognition and analysis of the stock market) where the main goal is to directly predict the dependent variable of interest, without focusing on the underlying relationships between the explanatory variables. The statistical methods developed in the ML literature have been particularly successful in “Big Data” settings, where we have either information on a large number of units, or many pieces of information on each unit (or both). The aim of this course is to present Machine Learning Techniques using an econometric perspective. During this course, students will learn the various concepts and techniques intensively used in the Machine Learning literature such as random trees, random forests, boosting, neural networks and deep learning, and their natural extensions to time series analysis and causal inference, with the complement of many practical examples. By the end of this course students are expected to be able to master and implement most of these techniques on real data problems using the statistical software R.

Course Prerequisites

Intermediate knowledge of econometrics, statistics and linear algebra.

Teaching Methods

The course is organized in 10 lectures and will cover the following topics. - Introduction, basic concepts and definitions. History and Foundations of Econometric and Machine Learning Models. - Statistical Learning: Supervised Versus Unsupervised Learning, Regression Versus Classification Problems. Assessing Model Accuracy: Quality of Fit measures and the Bias-Variance Trade-Off. - Linear Regression Models: Refresh, Extensions and Potential Problems. - Classification: Linear Methods, Logistic Regression, Linear Discriminant Analysis. - Model Validation and Selection. Shrinkage Methods: Ridge Regression and LASSO. Dimension Reduction Methods: PCA and PLS. - Non-linear models: Polynomial Regression, Regression and Smoothing Splines. - Tree-Based Methods - Neural Networks and Deep Learning - Support Vector Machines and Flexible Discriminants - Extension of Machine Learning Techniques to Time Series and Causal Inference.

Assessment Methods

The final assessment will be based for 30% on take-home exercises in which students will be asked to apply on real data examples the techniques learned during the lectures. The other 70% of the final mark will based on an oral presentation of a team report (with questions and answers) on the topics covered during the lectures. No partial exams will be held for this course.

Contents

The course is organized in 10 lectures and will cover the following topics. - Introduction, basic concepts and definitions. History and Foundations of Econometric and Machine Learning Models. - Statistical Learning: Supervised Versus Unsupervised Learning, Regression Versus Classification Problems. Assessing Model Accuracy: Quality of Fit measures and the Bias-Variance Trade-Off. - Linear Regression Models: Refresh, Extensions and Potential Problems. - Classification: Linear Methods, Logistic Regression, Linear Discriminant Analysis. - Model Validation and Selection. Shrinkage Methods: Ridge Regression and LASSO. Dimension Reduction Methods: PCA and PLS. - Non-linear models: Polynomial Regression, Regression and Smoothing Splines. - Tree-Based Methods - Neural Networks and Deep Learning - Support Vector Machines and Flexible Discriminants - Extension of Machine Learning Techniques to Time Series and Causal Inference.

Course Language

English

More information

The syllabus can be subject to modifications and changes during the course. Please check periodically the course page on e-learning for possible changes and communications by the instructor.

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