Gabriele ACCARINO

Gabriele ACCARINO

Contract Professor of High Performance Computing and Research Fellow

Area di competenza:

Statistics, Machine Learning, Deep Learning, Climate Science, Hybrid modelling

Orario di ricevimento

Su appuntamento

Recapiti aggiuntivi

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Curriculum Vitae

Gabriele Accarino is Contract Professor of High Performance Computing and Research Fellow at the Department of Engineering for Innovation at the University of Salento, Italy. His research activities mainly concern the design and development of cutting-edge Machine Learning-based modelling solutions to address the convergence between Machine Learning and Climate Science. He received the Master's Degree in Computer Engineering cum laude at the University of Salento, in 2018. Afterwards, he entered a Ph.D. program at the Department of Biological and Environmental Sciences and Technologies (DiSTeBA), exploring Machine Learning for application to the Climate Science domain. Since May 2018, he has been collaborating with the Advanced Scientific Computing (ASC) division and the Machine Learning group of the Euro-Mediterranean Center on Climate Change (CMCC) Foundation. Moreover, he has been involved in several European projects such as IS-ENES-3, eFlows4HPC and InterTwin. He is also author of several research papers, published in peer-reviewed international journals, leveraging Machine Learning approaches for climate science and epidemiological modelling.

Didattica

A.A. 2022/2023

SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI

Corso di laurea INFERMIERISTICA (ABILITANTE ALLA PROFESSIONE SANITARIA DI INFERMIERE)

Tipo corso di studio Laurea

Crediti 2.0

Ripartizione oraria Ore totali di attività frontale: 24.0

Anno accademico di erogazione 2022/2023

Per immatricolati nel 2022/2023

Anno di corso 1

Struttura DIPARTIMENTO DI SCIENZE E TECNOLOGIE BIOLOGICHE ED AMBIENTALI

Percorso COMUNE/GENERICO

A.A. 2021/2022

HIGH PERFORMANCE COMPUTING

Degree course COMPUTER ENGINEERING

Course type Laurea Magistrale

Language INGLESE

Credits 9.0

Teaching hours Ore totali di attività frontale: 81.0

Year taught 2021/2022

For matriculated on 2020/2021

Course year 2

Structure DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Subject matter PERCORSO COMUNE

Location Lecce

Torna all'elenco
SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI

Corso di laurea INFERMIERISTICA (ABILITANTE ALLA PROFESSIONE SANITARIA DI INFERMIERE)

Settore Scientifico Disciplinare ING-INF/05

Tipo corso di studio Laurea

Crediti 2.0

Ripartizione oraria Ore totali di attività frontale: 24.0

Per immatricolati nel 2022/2023

Anno accademico di erogazione 2022/2023

Anno di corso 1

Semestre Primo Semestre (dal 03/10/2022 al 20/01/2023)

Lingua

Percorso COMUNE/GENERICO (999)

SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI (ING-INF/05)
HIGH PERFORMANCE COMPUTING

Degree course COMPUTER ENGINEERING

Subject area ING-INF/05

Course type Laurea Magistrale

Credits 9.0

Teaching hours Ore totali di attività frontale: 81.0

For matriculated on 2020/2021

Year taught 2021/2022

Course year 2

Semestre Secondo Semestre (dal 01/03/2022 al 10/06/2022)

Language INGLESE

Subject matter PERCORSO COMUNE (999)

Location Lecce

Basic requirements 

  • Linear Algebra calculus
  • Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program
  • Programming skills (any programming language and paradigm)

Preferred requirements

  • Statistics
  • Python programming skills
  • Data handling and preparation techniques
  • Knowledge of data science and visualization Python libraries (numpy, sklearn, pandas, scipy, matplotlib)

The course provides a broad introduction to Machine Learning theory and practice for supervised learning applications.

Main topics:

  • Linear regression (univariate/multivariate)
  • Normal equation
  • Gradient descent algorithm
  • Polinomial regression
  • Regularization techniques (Ridge/Lasso)
  • Trade-off between high bias and high variance
  • Logistic regression (univariate/multivariate; single class/multi class)
  • Artificial Neural Networks and back-propagation
  • Design and implementation of an Artificial Neural Network in the Keras Deep Learning framework

Hands-on are also organized to provide students the capacity to develop specific use cases, using the Python programming language and the Jupiter Notebook environment.

KNOWLEDGE AND UNDERSTANDING:

By the end of the course, students will gain knowledge of the key theoretical and practical in the context of Machine Learning.

 

APPLYING KNOWLEDGE AND UNDERSTANDING:

By the end of the course, students will know how to use the acquired notions for the efficient use to design and implement Machine Learning algorithms for both regression and classification tasks.

 

MAKING JUDGEMENTS

By the end of the course, students will be able to critically assess the different Machine Learning approaches depending on the specific problem, being also able to assess their goodness.

 

COMMUNICATION SKILLS:

By the end of the course, students will be able to use a clear language and an adequate scientific terminology to argue on the topics dealt with in the course.

 

LEARNING SKILLS:

Students will be able to classify, schematize, summarize and process the acquired knowledge. By the end of the course, students will have the appropriate skills to develop and widen their knowledge of Machine Learning, with particular regard to the use of reference documentation and other information available online.

The credits of this course are obtained by means of an oral exam, that will assess the overall learning results achieved by the student.

Students (both attending and non-attending) will be asked three questions, one of which is aimed at checking problem solving skills and the student's ability to use the theoretical and practical notions acquired.

The final score will consider:

 

• The level of theoretical/practical notions acquired (50%);

• The ability to use the theoretical/practical notions acquired (30%);

• The ability to make autonomous assessments (10%);

• The communication skills (10%).

 

Honours are awarded to students mastering the topics covered in the course.

It is important to notice that, students interactions throughout the course will be also taken into consideration in the final evaluation process.

The oral exam is aimed at verifying to what extent the student has gained knowledge and understanding of the selected topics of the course and is able to communicate about his understanding. Students, divided into small groups, will also get hands-on experience, developing small projects on specific topics of the course. The max final vote is expressed as 30/30 with the possibility to get the laude.

  • Machine Learning course by Andrew Ng (Stanford)
  • Goodfellow, I. et al., 2016. Deep Learning
HIGH PERFORMANCE COMPUTING (ING-INF/05)

Pubblicazioni

Profilo ORCID

Profilo ResearchGate

Profilo GoogleScholar

Temi di ricerca

The research activities mainly concern the design and development of cutting-edge Machine Learning-based modelling solutions to address the convergence between Machine Learning and Climate Science.