MLCC 2019
Machine Learning Crash Course
Genova, June 17-21

Course at a Glance

The course will be held on June 17th-21st at DIBRIS (University of Genova, Italy)

Machine Learning is a key to develop intelligent systems and analyze data in science and engineering. Machine Learning engines enable intelligent technologies such as Siri, Kinect or Google self driving car, to name a few. At the same time, Machine Learning methods help deciphering the information in our DNA and make sense of the flood of information gathered on the web, forming the basis of a new “Science of Data”. This course provides an introduction to the fundamental methods at the core of modern Machine Learning. It covers theoretical foundations as well as essential algorithms. Classes on theoretical and algorithmic aspects are complemented by practical lab sessions.

This introductory course is suitable for undergraduate/graduate students, as well as professionals.

Related courses:

Basic Info


MLCC 2019 will take place at the Department of Informatics Bioengineering Robotics and Systems Engineering (DIBRIS) of the University of Genova, Via Dodecaneso 35, 16146 Genova.

Morning classes will be held in room 506. Afternoon labs will take place in rooms SW1, SW2, 217 and 218. Directions will be provided at the DIBRIS entrance in Via Dodecaneso 35.


Genova is the capital of Liguria, in the heart of Italian Riviera.


Here you can find a list of hotels near the department (~ 20' walk) or in the city centre (~20' by bus).


Here is a list of places where you can go for lunch.


For more info write to:
vigogna [at] dibris [dot] unige [dot] it
cristian [dot] rusu [at] iit [dot] it
raffaello [dot] camoriano [at] iit [dot] it


Lorenzo Rosasco

University of Genova
(also Istituto Italiano di Tecnologia and Massachusetts Institute of Technology)

lorenzo [dot] rosasco [at] unige [dot] it


Maurizio Filippone


Introduction to Gaussian Processes

Drawing meaningful conclusions on the way complex real life phenomena work and being able to predict the behavior of systems of interest require developing accurate and highly interpretable mathematical models whose parameters need to be estimated from observations. In modern applications, however, we are often challenged with the lack of such models, and even when these are available they are too computational demanding to be suitable for standard parameter optimization/inference methods.
This tutorial will introduce probabilistic models based on Gaussian processes as attractive tools to tackle these challenges in a principled way and to allow for a sound quantification of uncertainty. The tutorial will formally define Gaussian processes starting from the formulation of Bayesian linear models with infinite basis functions, and draw connections with non-probabilistic kernel machines and deep neural networks.
Carrying out inference for Gaussian processes poses huge computational challenges that arguably hinder their wide adoption. In recent years, however, have been a considerable amount of novel contributions that are allowing Gaussian processes to be applied to problems at an unprecedented scale and to new areas where uncertainty quantification is of fundamental importance. This tutorial will expose attendees to such recent advances, trends and challenges in Gaussian process modeling and inference, and stimulate the debate about the role of Gaussian process models in solving complex modern machine-learning tasks where deep neural networks are currently the preferred choice.

Dougal Sutherland

Gatsby Computational Neuroscience Unit

Adversarial generative models of images

Generative models of images have made an extraordinary amount of progress over the past five years, moving from vaguely plausible images of handwritten digit to nearly-photorealistic pictures of imaginary people. This tutorial will cover the key line of work, generative adversarial networks and their variants. We will discuss the original algorithm, theoretical issues with its foundations, and various approaches to resolving them, including the Wasserstein GAN and recent kernel-based improvements.


Mon 17th8:30-9:30506Registration
9:30-11:00506Class 1Introduction to Machine Learning class_1
11:30-13:00506Class 2Local Methods and Model Selection class_2
13:00-14:00506 front areaLunchPizza and focaccia, offered by MLCC
14:00-16:00SW1-SW2-217-218Lab 1Local Methods for Classification Matlab | Python
Tue 18th9:30-11:00506Class 3Regularization Networks I: Linear Models class_3
11:30-13:00506Class 4Regularization Networks II: Kernels class_4
13:00-14:00506 front areaLunchPizza and focaccia, offered by MLCC
14:00-16:00SW1-SW2-217-218Lab 2Regularization Networks Matlab | Python
Wed 19th9:15-10:45506Tutorial 1Maurizio Filippone - Introduction to Gaussian Processes slides
10:45-11:00506PresentationLorenzo Rosasco presents MaLGa
11:00-11:30506 front areaCoffee breakCoffee and networking, offered by our Sponsors
11:30-13:00506Tutorial 2Dougal Sutherland - Adversarial generative models of images slides | (pdf)
13:00-16:00506 front areaLunchFood and networking, offered by our Sponsors
Thu 20th9:30-11:00506Class 5Dimensionality Reduction and PCA class_5
11:30-13:00506Class 6Variable Selection and Sparsity class_6
13:00-14:00506 front areaLunchPizza and focaccia, offered by MLCC
14:00-16:00SW1-SW2-217-218Lab 3PCA and Sparsity Matlab | Python
16:00-18:00506 front areaAperitivoDrinks and networking, offered by our Sponsors
Fri 21st9:30-11:00506Class 7Clustering class_7
11:30-13:00506Class 8Data Representation: Deep Learning class_8

Gold Sponsor

Digital Tree è l'ecosistema dedicato a Intelligenza Artificiale, Machine Learning e Cloud Computing e alla creazione di iniziative verticali in tutti gli ambiti culturali, professionali ed ogni settore di business.

STARTUP HUB All'interno del nostro incubatore, supportiamo le startup basate su tecnologia di Intelligenza Artificiale nel raggiungere il mercato ed affermarsi crescendo velocemente.

ACADEMY La nostra academy sviluppa attività formative per il territorio e per una vasta comunità di utenti, dai giovanissimi agli studenti universitari, alle aziende. L’obiettivo è di sviluppare competenze e capacità nel campo dell’innovazione, del Data Technology, con un focus specifico sull’Intelligenza Artificiale e il Machine Learning.

OPEN INNOVATION Offriamo alle aziende opportunità di innovazione, grazie al nostro ecosistema e alle startup che accompagniamo durante il percorso di incubazione.

Tutto nello stesso posto.

Silver Sponsors

Konica Minolta è la multinazionale giapponese leader nei settori della stampa, dei sistemi diagnostici e dell’industria sensing. Nel laboratorio R&D di Roma utilizziamo tecniche avanzate di Intelligenza Artificiale, Robotica e Machine Learning per creare nuove soluzioni e servizi che diventeranno i futuri prodotti di Konica Minolta.
AKQA is a communication agency that deals with customer experience. It is specialized in the design and implementation of the most innovative and engaging forms of interaction between brands and their customers. It works on all contact channels in a coherent and integrated way, transferring the native digital approach at all moments of interaction between brand and consumer.
With over 40 years of experience in economic research, quantitative analysis and model development, Prometeia is a global provider of consulting services and software solutions focused on Risk, Performance & Wealth Management. With over 800 industry experts, we serve more than 300 financial institutions in 20 countries, through a consolidated network of foreign branches and subsidiaries located in Europe, Turkey, Africa and the Middle East.
Wind Tre, led by Jeffrey Hedberg, is a top Italian mobile operator and among the main alternative operators in the fixed-line. It is part of the CK Hutchison group.
The company is a player of reference in fixed-mobile integration and in developing new generation fibre optic networks. In the mobile market, following network consolidation, it has 21,000 transmission sites.
In the Consumer segment, Wind and 3 brands characterize products and offers. Wind Tre Business is the brand for businesses and the PA. Wind Tre is "Top Employers Italia 2019".
Pirelli was founded in Milan in 1872 and today stands as a global brand known for its cutting edge technology, high-end production excellence and passion for innovation that draws heavily on its Italian roots.
With 19 production plants in 12 countries and a commercial presence in over 160, Pirelli has around 31,500 employees and had a turnover in 2018 of about 5.2 billion €.
It is among the world’s major producers of tyres and associated services and the only one focused solely on the Consumer tyre market, which includes tyres for cars, motorcycles and bicycles.
Pirelli, a Pure Consumer Tyre Company, has a particular focus on the High Value tyre market and is constantly engaged in the development of innovative products to address the most specific mobility needs of the final Consumer, such as Specialty and Super Specialty tyres.

Bronze Sponsor

Open Search Network, a member of the Italian National Association of Artificial Intelligence (AIIA), is a niche headhunting boutique, based in London, with a specific focus on STEM + (Statistic, Technology, Engineering, Mathematics and Economics) profiles in Italy and around Europe. Since its founding in 2013, has served an ever-growing number of clients in different industries. Our network of over 30,000 members is a community of data scientists, data engineers, devops, biodata data architects, research scientists, cyber security experts, from consultants to directors, up to the executive level.

Applications are now closed. Results have been emailed.

For any inquiries, please write to


Stefano Vigogna

University of Genova (UNIGE)
Laboratory for Computational and Statistical Learning (LCSL)

vigogna [at] dibris [dot] unige [dot] it

Cristian Rusu

Istituto Italiano di Tecnologia (IIT)
Laboratory for Computational and Statistical Learning (LCSL)

cristian [dot] rusu [at] iit [dot] it

Raffaello Camoriano

Istituto Italiano di Tecnologia (IIT)
Laboratory for Computational and Statistical Learning (LCSL)

raffaello [dot] camoriano [at] iit [dot] it