Theory and practice of learning from data

This course aims at providing an introductory and unifying view of learning from data (inductive Artificial Intelligence). The course will present an overview of the theoretical background of learning from data, including the most used algorithms in the field, as well as  practical applications. Teaching mode:  Theoretical lesson plus laboratories in Python using Google Colab [https://colab.research.google.com/](https://colab.research.google.com/) Program: - Inference: induction, deduction, and abduction - Statistical inference - Machine Learning - Deep Learning (and Transfer Learning) - GenAI - Model selection and error estimation References: - C. C. Aggarwal "Data Mining - The textbook" 2015 - T. Hastie et. al "The Elements of Statistical Learning: Data Mining, Inference, and Prediction" 2009. - S. Shalev-Shwartz et. al "Understanding machine learning: From theory to algorithms" 2014 - C. M. Bishop et. al "Deep learning: Foundations and concepts" 2023 - D. Foster. "Generative deep learning". 2022. - L. Oneto "Model Selection and Error Estimation in a Nutshell" 2020