Machine learning, statistical learning, deep learning and artificial intelligence

A.A. 2021/2022
Crediti massimi
Ore totali
INF/01 SECS-S/01
Obiettivi formativi
The course introduces students to the most important algorithmical and statistical machine learning tools. The first part of the course focuses on the statistical foundations and on the methodological aspects. The second part is more hands-on, with laboratories to help students develop their software skills.
Risultati apprendimento attesi
Upon completion of the course students will be able to:
1. understand the notion of overfitting and its role in controlling the statistical risk
2. describe some of the most important machine learning algorithms and explain how they avoid overfitting
3. run machine learning experiments using the correct statistical methodology
4. provide statistical interpretations of the results.
Programma e organizzazione didattica

Edizione unica

Secondo semestre
The course requires basic knowledge in calculus, linear algebra, programming and statistics.
Modalità di verifica dell’apprendimento e criteri di valutazione
For the module Machine learning, the exam consists in writing a paper of about 10-15 pages containing either a report describing experimental results (experimental project) or a in-depth analysis of a theoretical topic (theory project). The paper will be discussed in an oral examination, in which students will be also asked questions on the rest of the syllabus. The final grade is computed by combining the project evaluation and the oral discussion. As a function of the number of students attending the course, the oral discussion may be replaced by a written test.

For the Module Statistical Learning, Deep Learning and Artificial Intelligence, the exam consists in preparing two individual projects using the package R, one on supervised and one on unsupervised learning. The projects will be discussed in an oral examination, in which students will be asked to explain and discuss the methodological choices, the code, the results. The ability to communicate and the critical ability to interpret the results will be evaluated. The grade is computed by combining the projects evaluation and the oral examination.

The final grade is the mean of the grades obtained in each module.
Module Machine Learning
2. The Nearest Neighbour algorithm
3. Tree predictors
4. Statistical learning
5. Hyperparameter tuning and risk estimates
6. Risk analysis of Nearest Neighbour
7. Risk analysis of tree predictors
8. Consistency, surrogate functions, nonparametric algorithms
9. Linear predictors
10. Online gradient descent
11. From sequential risk to statistical risk
12. Kernel functions
13. Support Vector Machines
14. Stability bounds and risk control for SVM
15. Boosting and ensemble methods
16. Neural networks and deep learning
Metodi didattici

The goal of this course is to provide a methodological foundation to machine learning. The emphasis is on the design and analysis of learning algorithms with theoretical performance guarantees.
Materiale di riferimento
The main reference are the lecture notes available through the link

A further reference is the textbook: Shai Shalev-Shwartz e Shai Ben-David, Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, 2014.
Module Statistical Learning, Deep Learning and Artificial Intellingence
Introduction to Statistical Learning
2. Cross Validation and Bootstrap
3. Variable Selection, Ridge and Lasso Regression
4. Linear Models
5. Non Linear Models
6. Logistic Regression and classification Methods
7. Classification and Regression Trees, bagging, boosting and Random Forest
8. Unsupervised learning (Clustering, PCA)
9. Brief notes on neural networks (tentative)
10. Brief notes on the association rules (tentative)
Metodi didattici
Lectures and Lab sessions

The goal of this module is to provide a methodological and practical overview to statistical learning methods. The emphasis is on the applications.

Optional group work will be offered to get familiar with the software and increase practical skills.
Materiale di riferimento
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning, Springer.

A further reference is the textbook:
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media.
Moduli o unità didattiche
Module Machine Learning
Lezioni: 40 ore

Module Statistical Learning, Deep Learning and Artificial Intellingence
Lezioni: 40 ore
Docente: Salini Silvia

Mercoledì 9:30-12:30
via Celoria 18, stanza 7007
A partire dal 20 settembre 2021 il ricevimento sarà in presenza il martedì dalle 10.30 alle 12 e il venerdì dalle 10.30 alle 12.00. In caso i necessità è possibile prendere appuntamento, nei medesimi orari, per il ricevimento via Teams.
DEMM, stanza 30, 3° p