Insegnamento a.a. 2026-2027

1.       Introduction: AI in Economics and Social Sciences

2.      Applications of Supervised Machine Learning

a)       Understanding and Predicting Demand

b)      Evaluating Workers' Performance

c)       Forecasting Civil Unrest

d)      Machine Learning and Causality

3.      Applications of Natural Language Processing

a)       Introduction: Natural Language Processing

b)      Analysing Central Bank Communications using Topic Models

c)       Measuring Knowledge Flows using Text Similarity

d)      Evaluating Bias in Text using Text Embeddings

e)       Determining Media Slant and Ideologies

f)       Quantifying Hate Speech and Free Speech in the Age of Social Media

g)      LLM Application in Economics

4.      Applications of Computer Vision

a)       Assessing Economic Growth using Night Lights

b)      Understanding Image Characteristics Using Computer Vision

·         Explain how machine learning can be used to answer new questions in the social sciences

·         Describe different machine learning methods and their application

·         Recognize potential applications of machine learning in real-world data

·         Illustrate the advantages and disadvantages of different machine learning methods in various contexts

·         Analyze the results of state-of-the-art economic research

·         Evaluate the validity of machine learning application

·         Assess how sensitive results are to the choice of machine learning algorithm

·         Critically weigh different machine learning algorithms against each other

·         Discuss how machine learning is broadening the fields of social science research.

·         Hypothesize about future applications of machine learning 

• Lectures
• Practical Exercises
• Individual works / Assignments
• Collaborative Works / Assignments

The learning experience of this course includes face-to-face lectures by the instructors. The instructor will additionally provide coding examples in class. The course will be further complemented with exercises that will allow the students to deepen their understanding of the methods discussed in class. In addition, students are also asked to prepare a group presentation on a research paper. These presentations are used to introduce students to the critical evaluation of empirical research and the contribution of papers to the scientific literature. Further, the presentations are intended to stimulate discussions about potential applications of machine learning to social science questions. This will allow the students to develop their own ideas about future machine learning projects.

The evaluation of attending students is based on an in-class group presentation (1/3) and a general exam (2/3). The general exam is open-book and centered on a research proposal that students prepare in advance. The proposal may cover any type of AI application to social science research questions.

The evaluation of non-attending students is based exclusively on a general exam. The exam is closed-book and consists of open-ended questions assessing students' knowledge of the papers and topics discussed in class.

Teaching materials are announced before the start of the course and indicated or uploaded to the Bboard platform.