Program Overview

MACHINE LEARNING AND DEEP LEARNING

Overview

In the information age, any system or device generates some form of data for diagnostic purposes or analysis. The course details the techniques for analyzing data in order to extract useful information and knowledge for decision making.

Aims and Content

Learning Outcomes

The course is designed to equip students with advanced knowledge and skills in the fields of machine learning and data analysis. Building upon foundational concepts, students delve into cutting-edge techniques and methodologies essential for tackling real-world problems in diverse domains. The course addresses a comprehensive review of fundamental machine learning algorithms, including supervised and unsupervised learning, and deep learning architectures. Through hands-on exercises and projects, students gain proficiency in implementing these algorithms using popular libraries.

Aims and Learning Outcomes

The student will be able to apply the acquired skills to a case study by deriving the model of the phenomenon that generated the data under analysis. During the course, the following skills will be developed:

• Personal competence
• Social competence
• Ability to learn to learn
• Competence in project creation
• Competence in project management

Prerequisites

• Coding (Matlab/Python/R)
• Linear algebra
• Probability and statistics

Teaching Methods

• Frontal lesson (approximately 50% to develop the ability to learn to learn)
• Laboratories (approximately 50% to develop personal competence)
• Possibility of a final project in pairs (to develop social competence, competence in project creation, and competence in project management)

For working students and students with certification of Specific Learning Disabilities (SLD), disabilities, or other special educational needs are advised to contact the instructor at the beginning of the course to arrange teaching and examination methods that, while respecting the teaching objectives, take into account individual learning styles.

Syllabus/Content

1. Statistical inference
2. Supervised, Semisupervised, and Unsupervised Learning
3. Statistical Learning Theory
4. Algorithmi Shallow di Machine Learning (examples in Python language)
5. Algorithmi Deep di Machine Learning (examples in Python language)
6. Generative AI
7. Model Selection and Error Estimation

Recommended Reading/Bibliography

• T. Hastie, R. Tibshirani, J. Friedman "The Elements of Statistical Learning: Data Mining, Inference, and Prediction" 2009
• S. Shalev-Shwartz, S. Ben-David "Understanding machine learning: From theory to algorithms" 2014
• C. M. Bishop, H. Bishop "Deep learning: Foundations and concepts" Springer Nature, 2023
• L. Oneto "Model Selection and Error Estimation in a Nutshell" 2020

Teachers and Exam Board

• Luca Oneto
• Davide Anguita

Exam Board

• Luca Oneto (President)
• Fabio Roli
• Davide Anguita (President Substitute)

Lessons

Lessons Start

The timetable for this course is available on the Portale EasyAcademy.

Exams

Exam Description

Oral by appointment.

Assessment Methods

The student will solve a real problem at will by applying the techniques learned during the course.

Exam Schedule

• Date: 13/02/2026
• Time: 07:00
• Location: GENOVA
• Degree type: Esame su appuntamento
• Note:
• Date: 17/09/2026
• Time: 07:00
• Location: GENOVA
• Degree type: Esame su appuntamento
• Note:

Additional Information

Agenda 2030 - Sustainable Development Goals

• Industry, innovation, and infrastructure

OpenBadge

• SOFT SKILLS - Gestione progettuale base 1 - A
• SOFT SKILLS - Imparare a imparare avanzato 1 - A
• SOFT SKILLS - Personale avanzato 1 - A
• SOFT SKILLS - Sociale avanzato 1 - A
• SOFT SKILLS - Creazione progettuale avanzato 1 - A