This module focuses on applying the experimental scientific method to Artificial Intelligence contexts. Students will learn to formulate and test hypotheses using AI algorithms.
Overview
The course integrates theoretical foundations with practical laboratory work, emphasizing hands-on experience with AI methods and experimental design.
Course Resources
Online Materials
- Official Syllabus: Complete course details, learning objectives, and lecture structure are available on the official course page.
- GitHub Organization: ai-disi-unibo contains all course materials and student projects.
- Project Template: ai-progetto-template is the starting template for student projects.
- E-learning Platform: Additional resources and course materials are available on Virtuale Unibo.
Recommended Textbooks & References
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"Dive into Deep Learning" by Zhang, Lipton, Li, and Smola — A comprehensive resource that complements course topics with practical examples and applications. Freely available online: Dive into Deep Learning.
@book{zhang2023dive, title={Dive into Deep Learning}, author={Zhang, Aston and Lipton, Zachary C. and Li, Mu and Smola, Alexander J.}, year={2023}, publisher={Cambridge University Press}, note={\url{https://d2l.ai}} } -
F. Rosenblatt's Perceptron: Interactive notebook demonstrating fundamental concepts in neural networks.
Lectures
December 10, 2025
Topic: AI as a scientific experimentation process
- Introduction to the problem
- Rule-based approaches vs. data-driven approaches
- Implementation and discussion of methods
Materials:
Examination
The exam consists of a group discussion on your project development. This assessment applies to both Module I and Module II.
For detailed requirements and submission guidelines, see the project page.