Machine learning, statistical learning, deep learning and artificial intelligence
A.A. 2019/2020
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.
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.
Periodo: Secondo semestre
Modalità di valutazione: Esame
Giudizio di valutazione: voto verbalizzato in trentesimi
Corso singolo
Questo insegnamento non può essere seguito come corso singolo. Puoi trovare gli insegnamenti disponibili consultando il catalogo corsi singoli.
Programma e organizzazione didattica
Edizione unica
Responsabile
Periodo
Secondo semestre
Prerequisiti
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 asked detailed questions about the algorithms used in the project, and also more high-level questions on the rest of the syllabus. The grade is computed by combining the project evaluation and the oral examination.
For the Module Statistical Learning, Deep Learning and Artificial Intelligence, the exam consists in preparing two assigments, using the package R, assigned during the course. The assignments will be discussed in an oral examination, in which students will be asked to explain and dicuss the methodological choices and the code. The grade is computed by combining the assigments evaluation and the oral examination.
The final grade is the mean of the grades obtained in each module.
For the Module Statistical Learning, Deep Learning and Artificial Intelligence, the exam consists in preparing two assigments, using the package R, assigned during the course. The assignments will be discussed in an oral examination, in which students will be asked to explain and dicuss the methodological choices and the code. The grade is computed by combining the assigments evaluation and the oral examination.
The final grade is the mean of the grades obtained in each module.
Module Machine Learning
Programma
Introduction
The Nearest Neighbour algorithm
Tree predictors
Statistical learning
Cross validation
Risk analysis of Nearest Neighbour
Risk analysis of tree predictors
Consistency and nonparametric algorithms
Linear classification
Online gradient descent
From sequential risk to statistical risk
Kernel functions
Support Vector Machines
Stability bounds and risk control in SVM
Boosting
Logistic regression
The Nearest Neighbour algorithm
Tree predictors
Statistical learning
Cross validation
Risk analysis of Nearest Neighbour
Risk analysis of tree predictors
Consistency and nonparametric algorithms
Linear classification
Online gradient descent
From sequential risk to statistical risk
Kernel functions
Support Vector Machines
Stability bounds and risk control in SVM
Boosting
Logistic regression
Metodi didattici
Lectures
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.
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 ncesa-bianchismml.ariel.ctu.unimi.it/
A further reference is the textbook: Shai Shalev-Shwartz e Shai Ben-David, Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, 2014.
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
Programma
Prediction of Quantitative Variables (Linear Regression, Ridge Regression, LASSO, Regression Trees, Neural Networks)
Unsupervised learning (Association rules, Clustering, PCA)
Classification hands-on
Unsupervised learning (Association rules, Clustering, PCA)
Classification hands-on
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.
The goal of this module is to provide a methodological and practical overview to statistical learning methods. The emphasis is on the applications.
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.
For further examples and readings:
Azzalini, Adelchi, and Bruno Scarpa. Data analysis and data mining: An introduction. OUP USA, 2012.
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.
For further examples and readings:
Azzalini, Adelchi, and Bruno Scarpa. Data analysis and data mining: An introduction. OUP USA, 2012.
Moduli o unità didattiche
Module Machine Learning
INF/01 - INFORMATICA - CFU: 6
Lezioni: 40 ore
Docente:
Cesa Bianchi Nicolo' Antonio
Turni:
-
Docente:
Cesa Bianchi Nicolo' Antonio
Module Statistical Learning, Deep Learning and Artificial Intellingence
SECS-S/01 - STATISTICA - CFU: 6
Lezioni: 40 ore
Docente:
Salini Silvia
Turni:
-
Docente:
Salini SilviaDocente/i
Ricevimento:
Il ricevimento studenti è il martedì dalle 10.00 alle 13.00 o in presenza o via Teams (meglio fissare un appuntamento) - Il ricevimento di martedì prossimo, per altri impegni accademici, non sarà svolto. Contattare il docente per un altro appuntamento.
DEMM, stanza 30, 3° p oppure su Teams