Course: Theory and Practice of Learning from Data
Hours: about 20
Teachers: Luca Oneto <firstname.lastname@example.org>
Schedule: held in 2022 and will be held again in 2024
Exam: Small presentation (max 30 min) on how the concepts presented in the course ca be used/extended during the student PhD.
This course aims at providing an introductory and unifying view of information extraction and model building from data, as addressed by many research fields like DataMining, Statistics, Computational Intelligence, Machine Learning, and PatternRecognition. 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.
Theoretical lesson plus laboratories in Python using Google Colab https://colab.research.google.com/
Inference: induction, deduction, and abduction
Model selection and error estimation
Implementation and Applications
C. C. Aggarwal "Data Mining - The textbook" 2015
T. Hastie, R.Tibshirani, J.Friedman "The Elements of Statistical Learning: Data Mining, Inference, and Prediction" 2009.
S. Shalev-Shwartz, S. Ben-David "Understanding machine learning: From theory to algorithms" 2014
I. Goodfellow, Y. Bengio, A. Courville "Deep learning" 2016
L. Oneto "Model Selection and Error Estimation in a Nutshell" 2020