Program Overview

OPERATIONS RESEARCH AND MACHINE LEARNING

Overview

This teaching unit is structured into two modules: Operations Research (OR) and Machine Learning. The OR module introduces models and methods from Operational Research to provide students with tools to address decision-making problems. The Machine Learning module presents the main machine learning methodologies aimed at pattern recognition, in particular for the classification of data from signals and images.

Aims and Content

Learning Outcomes

This teaching unit aims to provide students with knowledge of Operations Research models and methods and of Machine Learning methods applied to data, signal, and image recognition. Students will learn linear programming techniques, integer programming, graph theory, and network flow models. In the Machine Learning area, students will learn to characterize the distribution of a data set, reduce its dimensionality, and apply various classification techniques.

Prerequisites

• Operations Research: Basic knowledge of mathematical analysis, geometry, and computer science.
• Machine Learning: Calculus, probability theory, random variables, and matrix calculus.

Modules

• MACHINE LEARNING FOR PATTERN RECOGNITION
• OPERATIONS RESEARCH

Teaching Materials

• AULAWEB

Teachers and Exam Board

• MASSIMO PAOLUCCI
• SEBASTIANO SERPICO
• MARTINA PASTORINO

Exam Board

• SEBASTIANO SERPICO (President)
• ABDUL BASIT
• GABRIELE MOSER (President Substitute)
• MASSIMO PAOLUCCI (President Substitute)
• MARTINA PASTORINO (President Substitute)

Exams

Exam Description

• The exam for the Operations Research module consists of a written test.
• The exam for the Machine Learning module consists of a written test and an oral part.

Assessment Methods

• The Operations Research exam requires students to solve exercises, answer theoretical questions, and formulate simple combinatorial decision-making problems.
• The Machine Learning written test includes multiple-choice questions, open-ended questions, and simple problems. The oral part requires a deeper discussion of methods and the solution of more complex problems.

Exam Schedule

• Data appello | Orario | Luogo | Degree type | Note | Subject
  • 16/01/2026 | 15:30 | GENOVA | Scritto + Orale | | MACHINE LEARNING FOR PATTERN RECOGNITION
  • 09/02/2026 | 16:00 | GENOVA | Scritto + Orale | | MACHINE LEARNING FOR PATTERN RECOGNITION
  • 19/06/2026 | 16:00 | GENOVA | Scritto + Orale | | MACHINE LEARNING FOR PATTERN RECOGNITION
  • 09/07/2026 | 16:00 | GENOVA | Scritto + Orale | | MACHINE LEARNING FOR PATTERN RECOGNITION
  • 07/09/2026 | 15:00 | GENOVA | Scritto + Orale | | MACHINE LEARNING FOR PATTERN RECOGNITION
  • 09/01/2026 | 09:00 | GENOVA | Scritto | | OPERATIONS RESEARCH
  • 04/02/2026 | 08:30 | GENOVA | Scritto | | OPERATIONS RESEARCH
  • 04/06/2026 | 08:30 | GENOVA | Scritto | | OPERATIONS RESEARCH
  • 01/07/2026 | 09:00 | GENOVA | Scritto | | OPERATIONS RESEARCH
  • 17/09/2026 | 08:30 | GENOVA | Scritto | | OPERATIONS RESEARCH

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