Course Topics
The course focuses on fundamental Machine Learning techniques, combining theoretical instruction with practical application. The course covers basic supervised and unsupervised learning methods, with lectures devoted to conceptual understanding and labs aimed at hands-on implementation using real-world datasets. Students begin by learning to represent data and manage various feature types, then progress to building predictive models through supervised learning and discovering data groupings through unsupervised learning. Emphasis is placed on evaluating model quality and addressing challenges related to generalization. A significant portion of the course involves implementing algorithms using Python libraries such as Scikit-learn and SciPy, enabling students to tackle a variety of machine learning tasks across diverse application domains.

Teaching format
This is a project and lab-based module. It consists of frontal lectures, exercises in lab, case study analysis and the development of a project.

Educational objectives
Knowledge and Understanging: - D1.10: Know the principles of artificial intelligence and potentials and limits of intelligent systems in various application domains. Applying knowledge and understanding: - D2.12: Be able to adopt programming techniques of artificial intelligence to solve problems of computer science. - D2.14: Be able to apply the tools of statistics and probability theory to solve information technology issues Making judgements: - D3.1: Be able to collect and interpret useful data and to judge information systems and their applicability. - D3.2: Be able to work autonomously according to the own level of knowledge and understanding. Communication skills: - D4.1: Be able to use one of the three languages English, Italian and German, and be able to use technical terms and communication appropriately. Learning skills: - D5.1: Have developed learning capabilities to pursue further studies with a high degree of autonomy. - D5.3: Be able to follow the fast technological evolution and to learn cutting edge IT technologies and innovative aspects of last generation information systems.

Assessment
The course includes a written exam, worth 50% of the final grade, consisting of knowledge-check questions and problem-solving tasks, and a series of assignments, worth the remaining 50%, involving the implementation of machine learning algorithms on real-world datasets, the conduct of experiments, and the presentation of the results.

Evaluation criteria
The exam is assessed based on the correctness and clarity of the answers, the ability to summarize and critically evaluate the content, the ability to establish connections between topics, the quality of the argumentation, and the ability to solve problems. To pass the exam, students must obtain a minimum score of 18 out of 30 in both the theory and practical components.