Period: Second semester

Course unit contents:

* Gradient descent methods
* Ridge and LASSO regularization
* Deep neural networks and convolutional version
* Clustering
* Data visualization
* Energy-based models
* Restricted Boltzmann machines
* Combination of models: bagging, random forests, boosting, XGBoost

Planned learning activities and teaching methods: This course aims to expose the students to modern tools for classifying data and machine learning techniques so that they can apply those methods in lab experiences with computers. The first half of the course (24 hours) is reserved to learn general principles via applications. In contrast, the second half of the course allows the students, in small groups, to develop a deeper understanding of one specific subject by carrying out a small project.

The first half of the course includes theoretical explanations of some key procedures for data analysis or a class of algorithms, followed by exercise sessions in which the students will apply the new ideas on computers. This learning through practical experience is expected to improve understanding of theoretical tools. The numerical analysis includes adopting and modifying pre-built software or sketching simple algorithms from scratch.

At the end of the course, the student is expected to be able to: define the different machine learning methods used in the course; apply the most suitable method to solve a particular problem; identify key aspects of the procedure used and develop a combination of methods to improve the solution to the problem; judge the result obtained and explain and justify the use of particular methods.

The use of generative AI tools (e.g. ChatGPT) is permitted as support in code development and report writing. However, it is not allowed to generate entire codebases or full reports using AI. Any use must be explicitly declared and must comply with the academic integrity policies of the University of Padova and the Physics of Data degree program.

In addition to contacting the course instructor, students with disabilities, Specific Learning Disorders (SLD), Special Educational Needs (SEN), or other health conditions can reach out to the Student Services Office – Inclusion Unit to receive more information about opportunities to access teaching with specific support and tools.

The project work assesses transversal skills (teamwork, problem solving) and the practical application of knowledge; the oral presentation evaluates clarity of expression and critical thinking, covering descriptors D2–D5.

Last modified: Thursday, 7 August 2025, 10:39 AM