Syllabus del corso
Obiettivi
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).
Contenuti sintetici
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
Programma esteso
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 praticiPrerequisiti
Modalità didattica
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.
Materiale didattico
- 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.
Periodo di erogazione dell'insegnamento
Second semester
Modalità di verifica del profitto e valutazione
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 contribuisce al 60% della valutazione finale, la prova orale al restante 40%.
Orario di ricevimento
Su appuntamento
Aims
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).
Contents
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
Detailed program
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
Prerequisites
Basic knowledge on computer science, applied math, probability calculus and statistics
Teaching form
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.
Textbook and teaching resource
- 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.
Semester
Secondo semestre
Assessment method
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 amounts for 60% of the final mark, while the oral examination amounts for the remaining 40%.
Office hours
On appointment
Staff
-
Antonio Candelieri