Course Syllabus

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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

Unsupervised Learning

  • Concetti di similarità e distanza
  • Clustering
  • Outlier detection

Supervised Learning

  • Generalità: classificazione e regressione, metriche, 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
  • 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”

Una panoramica su Automated Machine Learning (AutoML)

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, video-registrate al fine di essere rese disponibili in formato digitale. 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:

  • Charu C. Aggarwal (2015). Data Mining – the Textbook
  • Carl Edward Rasmussen and Christopher K. I., Williams (2006). Gaussian Processes for Machine Learning.
  • Robert B. Gramacy (2020). Surrogates – Gaussian Processes Modeling, Design, and Optimization for the Applied Statistics.

Second semester

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 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.

Il progetto contribuisce al 60% della valutazione finale, la prova orale al restante 40%.


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

Unsupervised Learning

  • Similarity and distance
  • Clustering
  • Outlier detection

Supervised Learning

  • Classification and regression, metrics, 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 (lineari)

Supervised Learning for non-linear data

  • Non-linearity, VC dimensions, kernel-trick
  • 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”

Automated Machine Learning (AutoML): an overview

Exercises and examples

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

Teaching is achieved by classes: lectures will be video-recorded in order to make them digitally available. Lectures will abbress 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:

  • Charu C. Aggarwal (2015). Data Mining – the Textbook
  • Carl Edward Rasmussen and Christopher K. I., Williams (2006). Gaussian Processes for Machine Learning.
  • Robert B. Gramacy (2020). Surrogates – Gaussian Processes Modeling, Design, and Optimization for the Applied Statistics.

Secondo semestre

Assessment is organized on to tests:

  • the development of a project along with the preparation of an associated technical report, similar to a scientific paper,
  • an oral examination 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 project amounts for 60% of the final mark, while the oral examination amounts for the remaining 40%.


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Key information

Field of research
INF/01
ECTS
6
Term
Second semester
Activity type
Mandatory to be chosen
Course Length (Hours)
42
Language
Italian

Staff

    Teacher

  • Antonio Candelieri
    Antonio Candelieri

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Bibliography

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Enrolment methods

Manual enrolments
Self enrolment (Student)