This interactive workshop bridges the gap between theory and practice in machine learning. Participants will be introduced to the theoretical foundations of classical ML and DL models, followed by step-by-step demonstrations on real-world datasets. The session will highlight common approaches for model training, explore strategies to avoid typical pitfalls such as overfitting and data leakage, and provide practical guidance on best practices for robust experimentation. A complete use case will be developed and examined, giving participants end-to-end exposure to the ML workflow—from data preprocessing to evaluation. Demonstrations will be carried out live in Google Colab, allowing attendees to follow along, experiment with the code, and gain hands-on experience. No prior deep expertise is required, but a basic familiarity with Python will help participants make the most of the session.

Salvatore Tedesco is an Assistant Professor in UCC and Academic Member at Tyndall National Institute with a strong track record in wearable computing, pervasive computing, edge AI, edge intelligence, and applied artificial intelligence. He has proven expertise in deploying machine learning and edge analytics across diverse domains—including digital health, human motion analysis, physiological monitoring, Industry X.0, agri-tech, telecommunications —resulting in over 100 peer-reviewed publications and contributions to more than 35 funded research projects. He has successfully secured over €1.5 million in research funding as Principal Investigator and Co-PI, contributing to both foundational research and applied innovation.