Menù principale
B024317 - MACHINE LEARNING
Principali informazioni
Contenuto del corso
Libri di testo consigliati
Obiettivi Formativi
Prerequisiti
Metodi Didattici
Modalità di verifica apprendimento
Programma del corso
Anno Accademico 2019-20
Coorte 2018 - Laurea Magistrale in INGEGNERIA INFORMATICA
Anno di corso
Secondo Anno - Primo Semestre
Dipartimento di Afferenza
Ingegneria dell'Informazione
Tipo insegnamento
Attività formativa monodisciplinare
Settore Scientifico disciplinare
ING-INF/05 - SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI
Crediti Formativi
9
Ore Didattica
72
Periodo didattico
23/09/2019 ⇒ 20/12/2019
Frequenza Obbligatoria
No
Tipo Valutazione
Voto Finale
Contenuto del corso
mostra
Programma del corso
mostra
Docenza
Contenuto del corso
- Supervised learning.
- Generalized linear models. Neural networks.
- Support vector machines. Kernels.
- Computational learning theory.
- Unsupervised learning and generative models.
- Probabilistic graphical models.
- Relational learning.
- Neural networks and feature learning.
- Applications.
Libri di testo consigliati (Cerca nel catalogo della biblioteca)
<h4 class="mt-4">Textbooks</h4>
<dl class="row">
<dt class="col-sm-1">[GBC16]</dt>
<dd class="col-sm-11">I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. MIT
Press, 2016.</dd>
<dt class="col-sm-1">[A18]</dt>
<dd class="col-sm-11">Charu C. Aggarwal
Neural Networks and DeepLearning. Springer,
2018 (freely available from Unifi IP).</dd>
<dt class="col-sm-1">[HTF09]</dt>
<dd class="col-sm-11">T. Hastie, R. Tibshirani, and J. Friedman.
The Elements of Statistical Learning. Data
Mining, Inference, and Prediction. 2nd edition. Springer, 2009.</dd>
<dt class="col-sm-1">[B12]</dt>
<dd class="col-sm-11">D. Barber.
Bayesian Reasoning and Machine Learning. Cambridge University Press, 2012.</dd>
</dl>
<h4 class="mt-4">Other texts</h4>
<dl class="row">
<dt class="col-sm-1">[RN10]</dt>
<dd class="col-sm-11">Stuart Russell and Peter Norvig.
Artificial Intelligence: A Modern Approach. 3rd edition. Pearson, 2010.</dd>
<dt class="col-sm-1">[STC00]</dt>
<dd class="col-sm-11">John Shawe-Taylor and Nello Cristianini. Support Vector Machines and other kernel-based learning
methods, Cambridge University Press, 2000.</dd>
</dl>
<dl class="row">
<dt class="col-sm-1">[GBC16]</dt>
<dd class="col-sm-11">I. Goodfellow, Y. Bengio, A. Courville. Deep Learning. MIT
Press, 2016.</dd>
<dt class="col-sm-1">[A18]</dt>
<dd class="col-sm-11">Charu C. Aggarwal
Neural Networks and DeepLearning. Springer,
2018 (freely available from Unifi IP).</dd>
<dt class="col-sm-1">[HTF09]</dt>
<dd class="col-sm-11">T. Hastie, R. Tibshirani, and J. Friedman.
The Elements of Statistical Learning. Data
Mining, Inference, and Prediction. 2nd edition. Springer, 2009.</dd>
<dt class="col-sm-1">[B12]</dt>
<dd class="col-sm-11">D. Barber.
Bayesian Reasoning and Machine Learning. Cambridge University Press, 2012.</dd>
</dl>
<h4 class="mt-4">Other texts</h4>
<dl class="row">
<dt class="col-sm-1">[RN10]</dt>
<dd class="col-sm-11">Stuart Russell and Peter Norvig.
Artificial Intelligence: A Modern Approach. 3rd edition. Pearson, 2010.</dd>
<dt class="col-sm-1">[STC00]</dt>
<dd class="col-sm-11">John Shawe-Taylor and Nello Cristianini. Support Vector Machines and other kernel-based learning
methods, Cambridge University Press, 2000.</dd>
</dl>
Obiettivi Formativi
You will learn about several fundamental and some advanced algorithms for statistical learning, you will know the basics of computational learning theory, and will be able to design state-of-the-art solutions to application problems.
Prerequisiti
A good knowledge of a programming language, and a solid background in mathematics (calculus, linear algebra, and probability theory) are necessary prerequisites to this course. Previous knowledge of optimization techniques and statistics would be useful but not strictly necessary.
Metodi Didattici
Lectures and practical sessions.
Modalità di verifica apprendimento
B024317: 9 CFU
- There is a single oral final exam. You can choose the exam topic but you are strongly advised to discuss with me before you begin working on it.
- Typically, you will be assigned a set of papers to read and will be asked to reproduce some experimental results.
- You will be required to give a short (30 min) presentation during the exam. Please ensure that your presentation includes an introduction to the problem being addressed, a brief review of relevant literature, technical derivation of methods, and, if appropriate, a detailed description of experimental work. You are allowed to use multimedia tools to prepare your presentation. You are responsible for understanding all the relevant concepts and the underlying theory.
- You can work in groups of two to carry out experimental works (three is an exceptional number that you must motivate clearly). If you do so, please ensure that personal contributions to the overall work are clearly identifiable.
B024318: 6 CFU
- Same as above on the first 2/3 of the program and experimental work is optional