- Docente: Roberto Amadini
- Credits: 8
- SSD: INF/01
- Language: Italian
- Teaching Mode: Traditional lectures
- Campus: Bologna
- Corso: Second cycle degree programme (LM) in Digital Innovation Policies and Governance (cod. 5889)
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from Sep 17, 2025 to Nov 27, 2025
Learning outcomes
The course covers the fundamentals of Artificial Intelligence, including symbolic and sub-symbolic aspects, main machine learning techniques, and ethical, economic, and social implications. Students will gain practical problem-solving skills using specialized software tools and understand various machine learning models and training mechanisms.
Course contents
The course is divided into two parts.
The first part primarily focuses on "symbolic" artificial intelligence and covers the following topics at a high level:
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Introduction to Artificial Intelligence
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Intelligent Agents
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Search Problems and Constraint Satisfaction
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Knowledge Representation and Automated Reasoning
The second part mainly deals with machine learning and "sub-symbolic" artificial intelligence. The topics covered include:
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Supervised Machine Learning
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Deep Learning
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Unsupervised Learning, Generative AI, and Reinforcement Learning
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Natural Language Processing and Language Models
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Ethics and Safety in Artificial Intelligence
Readings/Bibliography
- Russell, Stuart J. Artificial intelligence a modern approach. Pearson Education, Inc., 2021.
- Wallace, Mark. Building decision support systems: using MiniZinc. Cham: Springer, 2020
Teaching methods
The course consists of lectures presented with slides, complementary activities, and exercises to prepare for the exam.
Classes are held entirely in person.
All teaching materials are made available on the course’s Virtuale platform.
Assessment methods
The knowledge acquired will be assessed through:
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a mandatory written exam, consisting of multiple-choice questions and exercises.
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an optional project, to be agreed upon with the instructor and possibly carried out in a group.
The written exam does not include programming exercises and is conducted on paper, without the aid of manuals, personal notes, or any electronic devices.
The optional project can be discussed at any time during the year, but only after passing the written exam. It can increase the exam grade by up to 3 points.
Six exam sessions per year are guaranteed, along with adapted assessments for students with learning disabilities (DSA).
Teaching tools
During the lectures, Python code will be shown using the Colab platform, and LLMs such as ChatGPT or similar tools will be used.
Office hours
See the website of Roberto Amadini