Course: Theory and Practice of Learning from Data
Credits: 5
Hours: about 20
Teachers: Luca Oneto <luca.oneto@unige.it>
Schedule: held in 2024 and will be held again in 2026
Where: TBD
Exam: Small presentation (max 30 min) on how the concepts presented in the course ca be used/extended during the student PhD.
Material: TBD
Course Description
Abstract:
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/
Program:
Inference: induction, deduction, and abduction
Statistical inference
Machine Learning
Deep Learning (and Transfer Learning)
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
L. Oneto "Model Selection and Error Estimation in a Nutshell" 2020