Course: Theory and Practice of Learning from Data
Hours: about 20
Teachers: Luca Oneto <email@example.com>
Tentative Schedule: TBD (course has been delivered on 2020 and will be delivered again in 2022)
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.
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