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Lo studente apprenderà le tecniche di Machine Learning più efficaci, comprendendo i fondamenti teorici di ogni tecnica e acquisendo il know-how per poterle applicare con successo alla risoluzione di problemi pratici. Sarà inoltre fornita una panoramica sulle più innovative soluzioni per l’identificazione del miglior algoritmo di Machine Learning e della sua configurazione ottimale (Automated Machine Learning – AutoML), dato un dataset. Lo strumento di riferimento per il corso sarà R, ma verranno anche presentate alcune soluzioni equivalenti in Python (ad esempio scikit-learn) e Java (ad esempio WEKA, KNIME).

Concetti basi del Machine Learning: tipologie di dati, istanze, features, tasks e scenarios, parametri e iper-parametri, misure di performance
Tecniche di apprendimento non-supervisionato
Tecniche di apprendimento supervisionato: classificazione e regressione
Modellare non-linearità nei dati: tecniche basate sul concetto di kernel
Automated Machine Learning: configurazione automatica di un modello di Machine Learning

Introduzione

  • Machine Learning scenarios & tasks, notazioni utili
  • Tipi di dati e problemi: tabular, streams, text, time-series, sequences, spatial, graph, web, social, immagini, distribuzioni

Unsupervised Learning

  • Concetti di similarità e distanza
  • Distanze tra punti: Minkowski e casi specifici (Manhattan, Euclidea, Chebyshev/Lagrange), Mahalanobis
  • Wasserstein: una distanza (non una divergenza!) tra distribuzioni di probabilità e/o nuvole di punti (datasets)
  • Clustering: approcci deterministici vs probabilistici; flat vs gerarchici; basati su distanza/similarità vs densità
  • Outlier and anomaly detection

Supervised Learning

  • I fondamenti del "learning": classificazione binaria, processo di generazione dei dati, concetto vs ipotesi, errore empirico vs errore di generalizzazione
  • Classificazione e regressione: metriche e tecniche di validazione (hold-out, k fold-cross, leave-one-out)
  • Approcci model-free/instance-based, un semplice algoritmo: k-nearest neighbors (KNN)
  • Approcci model-based: Support Vector Machine (lineari)

Supervised Learning per dati non-lineari

  • Non-linearità, VC dimensions, kernel-trick
  • Un richiamo a Decision Tree e Random Forest
  • Kernel-based learning: kernel-SVM e Gaussian Processes per classificazione e regressione
  • Dimensionlity reduction: Principal Component Analysis (PCA) e kernel-based PCA (kPCA)

L’approccio connessionista

  • Artificial Neural Networks: paradigma di apprendimento
  • Deep Learning: “a fraction of the connectionist tribe”
  • Modelli neurali Generativi: Auto-Encoder (AE) e Variational-AE (VAE), Generative Adversarial Network (GAN) e Wasserstein-GAN (WGAN), Transformer

Automated Machine Learning (AutoML)

  • Ottimizzazione degli iperparametri di un algoritmo di Machine Learning
  • Selezione del miglior algoritmo di Machine Learning e (simultanea) ottimizzazione dei suoi iperparametri

Esercizi ed esempi pratici

Si consiglia la conoscenza di elementi di base di informatica, matematica applicata, probabilità e statistica

L'intera attività formativa viene svolta attraverso lezioni in presenza. Le lezioni riguarderanno sia aspetti teorici che applicazioni pratiche, specificatamente l'utilizzo di librerie software e dati open.

  • Testo di riferimento: Mehryar Mohri, Afshin Rostamizadeh and Ameet Talwalkar (2018). Foundations of Machine Learning.
  • Slides e materiale didattico fornito dal docente

Altri tesi suggeriti:

  • Deisenroth, M. P., Faisal, A. A., & Ong, C. S. (2020). Mathematics for machine learning. Cambridge University Press.
  • Charu C. Aggarwal (2023). Neural Networks and Deep Learning – A Textbook
  • Robert B. Gramacy (2020). Surrogates – Gaussian Processes Modeling, Design, and Optimization for the Applied Statistics.
  • Charu C. Aggarwal (2015). Data Mining – the Textbook

Primo semestre - primo periodo

La modalità di verifica prevede le seguenti 2 prove:

  • lo svolgimento di un progetto con associata redazione di un rapporto tecnico, stile articolo scientifico,
  • un esame orale (individuale e obbligatorio) finalizzato a verificare il grado di comprensione degli argomenti trattati.

Il progetto può essere svolto in team (max 3 studenti per gruppo) ed i dataset oggetto delle attività saranno concordati con il docente a partire da piattaforme open quali OpenML, Kaggle o UCI Repository.
La qualità del progetto è stabilita sulla base del corretto utilizzo degli algoritmi di ML e all'analisi critiva dei risultati. L'esame orale è finalizzato alla verifica della comprensione di aspetti teorici e metodologici del ML.
Il progetto contribuisce al 60% della valutazione finale, la prova orale al restante 40%.

Non sono previste prove intermedie.

Su appuntamento

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The student will learn the most effective Machine Learning techniques, understanding the theoretical foundations of each technique and acquiring the know-how to successfully apply them to solving practical problems. An overview of the most innovative solutions for the identification of the best Machine Learning algorithm and its optimal configuration, given a dataset (Automated Machine Learning - AutoML), will also be provided. The reference tool for the course will be R, but some equivalent solutions will also be presented in Python (for example scikit-learn) and Java (for example WEKA, KNIME).

Machine Learning basics: types of data, instances, features, tasks and scenarios, parameters and hyper-parameters, performance measures
Unsupervised learning techniques
Supervised learning techniques: classification and regression
Modeling non-linearity in data: kernel-based techniques
Automated Machine Learning: automatic configuration of a Machine Learning model

Introduction

  • Machine Learning scenarios & tasks, useful notations
  • Types of data and problems: tabular, streams, text, time-series, sequences, spatial, graph, web, social, immagini, distribuzioni

Unsupervised Learning

  • Similarity and distance
  • Distances between points: Minkowski and special cases (Manhattan, Euclidean, Chebyshev/Lagrange), Mahalanobis
  • Wasserstein: a distance (not a divergence!) between probability distributions and/or point-clouds (i.e., datasets)
  • Clustering: deterministic vs probabilistic approaches; flat vs hierarchical; distance/similarity vs density -based
  • Outlier and anomaly detection

Supervised Learning

  • Foundations of "learning": binary classification, data generation process, concept vs hypothesis, empirical vs generalization error
  • Classification and regression: metrics and validation techniques (hold-out, k fold-cross, leave-one-out)
  • Model-free/instance-based approaches, a simple algorithm: the k-nearest neighbors (KNN)
  • Model-based approaches: Support Vector Machine (linear)

Supervised Learning for non-linear data

  • Non-linearity, VC dimensions, kernel-trick
  • A brief recall on Decision Tree and Random Forest
  • Kernel-based learning: kernel-SVM and Gaussian Processes, for classification and regression
  • Dimensionlity reduction: Principal Component Analysis (PCA) and kernel-based PCA (kPCA)

The connectionist approach

  • Artificial Neural Networks: learning paradigm
  • Deep Learning: “a fraction of the connectionist tribe”
  • Generative neural models: Auto-Encoder (AE) and Variational-AE (VAE), Generative Adversarial Network (GAN) and Wasserstein-GAN (WGAN), Transformer

Automated Machine Learning (AutoML): an overview

  • Hyeprparameter optimization of a Machine Learning algorithm
  • Algorithm selection and (simultaneous) hyperparameter optimization of a Machine Learning algorithm

Exercises and examples

Basic knowledge on computer science, applied math, probability calculus and statistics

Teaching is provided in-person. Lectures will address both theory and hands-on, specifically the adoption of open data and software libraries.

  • Reference textbook: Mehryar Mohri, Afshin Rostamizadeh and Ameet Talwalkar (2018). Foundations of Machine Learning.
  • Slides and materials provided by the lecturer

Other suggested texts:

  • Deisenroth, M. P., Faisal, A. A., & Ong, C. S. (2020). Mathematics for machine learning. Cambridge University Press.
  • Charu C. Aggarwal (2023). Neural Networks and Deep Learning – A Textbook
  • Robert B. Gramacy (2020). Surrogates – Gaussian Processes Modeling, Design, and Optimization for the Applied Statistics.
  • Charu C. Aggarwal (2015). Data Mining – the Textbook

First semester - First period

Assessment is organized on two tests:

  • the development of a project along with the preparation of an associated technical report, similar to a scientific paper,
  • an oral examination (individual and mandatory) aimed at assessing the degree of understanding of the course's topics.

The project can be performed by working in team (max 3 students per group) and the datasets to adopt, in agreement with the lecturer, will be selected among those available on open platforms such as OpenML, Kaggle or UCI Repository.
The quality of the project will be assessed according to the correct adoption of ML algorithms and the analysis of the results. Oral examination is devoted to assess the understanding of theoretical and methodological aspects of ML.
The project amounts for 60% of the final mark, while the oral examination amounts for the remaining 40%.

There are no mid-term review(s)

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Key information

Field of research
INF/01
ECTS
6
Term
First semester
Activity type
Mandatory to be chosen
Course Length (Hours)
42
Degree Course Type
2-year Master Degreee
Language
English

Staff

    Teacher

  • Antonio Candelieri
    Antonio Candelieri

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Enrolment methods

Manual enrolments
Self enrolment (Student)

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