TPLD (PhD)

Course: Theory and Practice of Learning from Data

Credits: 5

Hours: about 20

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

Schedule: 

• Tuesday 2nd of July 2024, 8:30-13:30

• Wednesday 3rd of July 2024, 8:30-13:30

• Wednesday 04th of July 2024, 8:30-13:30

• Friday 5th of July 2024, 8:30-13:30

Where: Online on Zoom https://zoom.us/ (ID: 83326598045 - Passcode: 589300)

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)

• 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