Daniele Spiga
(PG)
17/09/2018, 10:00
Daniele Bonacorsi
(BO)
17/09/2018, 10:20
An overview of (selected) use-cases for {Machine,Deep} Learning in various scientific disciplines is presented. Peculiarities of - and similarities across - disciplines are explored. Possible cross-fertilisations and synergic approaches, as well as the value of training paths, is also discussed.
Prof.
Andrea Bartolini
(Università di Bologna)
17/09/2018, 11:50
We hear it almost every day but we barely recognize, it’s the noisy fan of our laptop. We feel it with our hands in the summer, it’s our mobile getting too hot. It’s the heat dissipated by electronics devices. Digital processing elements, the heart of our smartphone, laptop, workstation and supercomputers dissipate power for flipping bits of information, this power increases the temperature of...
Dr
Stefano Calderan
(Oval Money)
17/09/2018, 14:00
Material is available here https://github.com/calde12/SOSC2018-PYTHON-HANDSON
Paolina Cerlini
(CIRIAF-CRC)
17/09/2018, 15:35
In the next future the new data centre of the European Centre for Medium-range Weather Forecasts (ECMWF) based in Reading U.K. will be moved to Bologna, Italy.
The building is to be delivered to ECMWF by 2019 and will host the Centre’s new supercomputers, whilst the Centre’s headquarters are to remain in the UK.
This centre will host one of the few global dataset of environmental data used...
Prof.
Elisa Ricci
(Università di Trento)
18/09/2018, 10:00
From self-driving cars to voice-activated intelligent assistants and robots reading and mimicking human emotions, machine learning
and deep learning are not only revolutionizing several industry sectors but they are slowly and steadily becoming part of our daily lives.
This talk will provide an overview of deep learning, namely of modern, multi-layered neural networks trained on big data. In...
Dr
Valentin Kuznetsov
(Cornell University (US))
18/09/2018, 11:30
The Data Science is an rapidly growing field of Information Technology.
In this talk we'll cover its origin, the connection to other IT fields
and cover details of Data Science fields from data acquisition,
processing to building Machine Learning models. We'll cover the
tools, technologies and techniques a Data Scientist use in their daily
activities. We'll also discuss algorithms and...
Prof.
Andrea De Simone
(sissa)
18/09/2018, 15:35
I introduce the basic concepts of unsupervised learning,
highlighting the differences with supervised learning.
The focus will be on three of the main tasks addressed
by unsupervised learning: cluster analysis, anomaly detection and
dimensional reduction.
For each of them, I describe the problem in general terms,
the ways to approach it, and some of the most popular
algorithms to solve it.
Prof.
Andrea De Simone
(sissa)
18/09/2018, 18:00
Hands-on material : https://github.com/de-simone/SOSC18_handson
Dr
Sandro Fiore
(Centro Euro-Mediterraneo sui Cambiamenti Climatici)
19/09/2018, 10:00
Marco Zanetti
(PD),
Maurizio Pierini
(CERN)
19/09/2018, 11:30
Dr
Maurizio Pierini
(CERN)
19/09/2018, 15:35
Dr
Maurizio Pierini
(CERN)
19/09/2018, 16:30
Tutorial material URL: https://www.dropbox.com/s/or43zo8imt52l0x/SOSC18_Tutorial.tar.gz?dl=0
Davide Salomoni
(CNAF)
20/09/2018, 09:00
Dr
Vaggelis motesnitsalis
(CERN)
20/09/2018, 10:00
The LHC experiments continue to produce a wealth of valu-
able High Energy Physics data, which oer numerous possibilities for
new discoveries. The IT Department at CERN provides Hadoop and
Spark services and works closely with the scientic communities in their
quest to analyze and understand these vast amounts of physics and in-
frastructure data. The number of CERN teams using these...
Daniele Spiga
(PG),
Diego Ciangottini
(PG),
Mirco Tracolli
(PG)
20/09/2018, 14:00
This session will include the usage of Dynamic On-Demand Analysis Service (DODAS) and as such will includes:
Containers Orchestration (Mesos)
Software Applications & Dependency description (TOSCA);
Cloud PaaS Orchestration (INDIGO-PaaS)
Dr
Valentin Kuznetsov
(Cornell University (US))
20/09/2018, 15:35
In this talk we'll cover materials and knowledge base of how to become a world
class Data Scientists. We'll use real kaggle competition dataset and build for it a set of ML models. During this session we'll introduce advanced concepts of
ML training, like feature embedding, data transformation and ML model
fine-tuning. We'll discuss how to work large datasets, techniques to avoid...
Daniele Spiga
(PG),
Diego Ciangottini
(PG),
Mirco Tracolli
(PG)
20/09/2018, 16:30
Dr
Alberto Di Meglio
(CERN)
21/09/2018, 09:00
CERN openlab as a model for R&D and Technology Transfer at CERN
Abstract: CERN openlab was created in 2001 as a way of establishing a new collaboration channel between CERN and ICT industry.
Over the past 17 years, it has become a reference for joint R&D and technology transfer at CERN in support of the future computing
and data requirements of the increasingly challenging LHC research...
Prof.
Franco Moriconi
(University of Perugia and ALEF Advanced Laboratory Economics and Finance srl)
21/09/2018, 10:00
Some applications of big data analytics techniques to the insurance business
Some examples are provided to illustrate how the insurance industry is currently facing the new paradigm of big data analytics. Methodologies for analyzing telematic car driving data are illustrated. In particular, it is shown how pattern recognition and machine learning techniques can be used to derive predictive...
Davide Salomoni
(CNAF)
21/09/2018, 12:30
