Acronym: TPLD (PhD)

Course: Theory and Practice of Learning from Data

Credits: 5

Hours: about 20

Teachers: Luca Oneto <luca.oneto@unige.it>

Schedule:  

• Monday 6th of July 2026 - 8:00-13:00

• Tuesday 7th of July 2026 - 8:00-13:00

• Wednesday 8th of July 2026 - 8:00-13:00

• Thursday 9th of July 2026 - 8:00-13:00

Where: Zoom (ID: 81445053813 - Passcode: 341014)

Exam: Small presentation (max 30 min) on how the concepts presented in the course ca be used/extended during the student PhD.

Material: LINK

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)

• 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