General Information
SSD: ING-INF/05
CFU: 8
Professor: Flavio Giobergia
Teaching Assistants: Matteo Berta, Claudio Savelli, Lorenzo Vaiani
Exam rules
The exam rules for A.Y. 2026/27 will be published here.
Teaching Material
Data science
This section contains the theory slides for the DSML course.
- Course introduction (slides)
- Introduction to data science & machine learning (slides)
- Data preprocessing (slides)
- Classification fundamentals (slides)
- Regression analysis (slides)
- Introduction to Deep Learning (slides)
- Association rules (slides)
- Clustering fundamentals (slides)
- Anomaly Detection (slides) (PDF notebook) (.ipynb notebook)
Python
This section contains the Python slides for the DSML course.
- NumPy (slides)
- Pandas (slides)
- Matplotlib (slides)
- Scikit-learn (Classification) (slides)
- Scikit-learn (Regression) (slides)
- Scikit-learn (Pre-processing) (slides)
- Scikit-learn (Clustering) (slides)
- PyTorch (slides)
Other material
- GitHub repository with exercises
Exercises
Other material
- Python introduction material (from A.Y. 2024/25)
Laboratory Material
This section will contain all the material for carrying out laboratories. No laboratory will be evaluated and assigned a mark, so no laboratory will give additional points to the final exam.
Team organization
Students will be divided into three groups based on course overlaps and surname. Please ensure you attend the correct slot to ensure there is enough space for everyone during the laboratories.
You can use the following rule:
- If you are a student of Bioquants, or your last name is from “AA” to “EU” ⇒ Monday, 10:00–13:00, LAIB4
- If you are a student of Numerical Optimization or Network Dynamics, or your last name is from “FA” to “PB” ⇒ Tuesday, 08:30–11:30, LAIB1
- If you are a student of Information System Security, or your last name is from “PC” to “ZZ” ⇒ Thursday, 10:00–13:00, Aula 10C