TPLD

Course: Theory and Practice of Learning from Data

Credits: 5

Hours: about 20

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

Tentative Schedule: TBD (course has been delivered on 2020 and will be delivered again in 2022)

Where: TBD

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

Course Description

Abstract:

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.

Program:

  • Inference: induction, deduction, and abduction

  • Statistical inference

  • Machine Learning

  • Deep Learning

  • Model selection and error estimation

  • Implementation and Applications

References:

  • 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