Introduzione al Machine Learning

The course will introduce concepts and methods of the two major families of tasks for learning from data. Project work in teams will be assigned along the course.

1) Introduction: the Knowledge Discovery process.

• All steps in a nutshell.
• Data understanding and Data exploration: methods and practicals case study on simple data sets iris e titanic

2) Unsupervised learning methods and practicals case study on simple data sets iris e titanic

• Clustering: intro clustering: K-Means clustering, hints on DBSCAN e Hierarchical clustering
• Pattern mining and Association Rules: a-priori pattern mining:

3) Supervised Learning: methods and practicals case study on simple data sets iris e titanic

• Classification: introduction, performance evaluation, a first simple classifier: Decision tree 
• Overview of advanced methods: Random Forest, Support Vector Machine, Neural Networks
•  Deep Learning  architecture and exemplar use cases: Recurrent Neural Networks, Generative adversarial networks, Transformers, and Graph Neural Networks 

4) Design principles and Trustworthy issues on AI systems based on Data Mining and Machine Learning: bias discovery and explainability 

The course will provide  an open lab to support students for project execution Didattica Integrativa (12 hours)

Scheduling: Monday: 14-16 Aula Fermi, Tuesday: 11-13 aula Bianchi Scienze.

The course starts on February 19th, 2023.

The exam will consist of a project work and its discussion