Marco PULIMENO

Marco PULIMENO

Ricercatore Universitario

Dipartimento di Ingegneria dell'Innovazione

Centro Ecotekne Pal. O - S.P. 6, Lecce - Monteroni - LECCE (LE)

Ufficio, Piano terra

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Recapiti aggiuntivi

Tel. ufficio: 0832297235 - 0832297304

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Didattica

A.A. 2022/2023

Degree course MATEMATICA

Course type Laurea Magistrale

Language INGLESE

Credits 6.0

Teaching hours Ore totali di attività frontale: 42.0

Year taught 2022/2023

For matriculated on 2022/2023

Course year 1

Structure DIPARTIMENTO DI MATEMATICA E FISICA "ENNIO DE GIORGI"

Subject matter MATEMATICA PER L'INTELLIGENZA ARTIFICIALE

Location Lecce

DATA MINING

Degree course MATEMATICA

Course type Laurea Magistrale

Language INGLESE

Credits 6.0

Teaching hours Ore totali di attività frontale: 42.0

Year taught 2022/2023

For matriculated on 2022/2023

Course year 1

Structure DIPARTIMENTO DI MATEMATICA E FISICA "ENNIO DE GIORGI"

Subject matter TEORICO-MODELLISTICO

Location Lecce

MOBILE APPLICATIONS DEVELOPMENT

Degree course DIGITAL HUMANITIES

Course type Laurea Magistrale

Language INGLESE

Credits 6.0

Teaching hours Ore totali di attività frontale: 42.0

Year taught 2022/2023

For matriculated on 2022/2023

Course year 1

Structure DIPARTIMENTO DI BENI CULTURALI

Subject matter COMUNE/GENERICO

PARALLEL ALGORITHMS

Degree course COMPUTER ENGINEERING

Course type Laurea Magistrale

Language INGLESE

Credits 9.0

Owner professor Massimo CAFARO

Teaching hours Ore totali di attività frontale: 81.0

  Ore erogate dal docente MARCO PULIMENO: 27.0

Year taught 2022/2023

For matriculated on 2021/2022

Course year 2

Structure DIPARTIMENTO DI INGEGNERIA DELL'INNOVAZIONE

Subject matter PERCORSO COMUNE

Location Lecce

Torna all'elenco

Degree course MATEMATICA

Subject area ING-INF/05

Course type Laurea Magistrale

Credits 6.0

Teaching hours Ore totali di attività frontale: 42.0

For matriculated on 2022/2023

Year taught 2022/2023

Course year 1

Semestre Secondo Semestre (dal 27/02/2023 al 09/06/2023)

Language INGLESE

Subject matter MATEMATICA PER L'INTELLIGENZA ARTIFICIALE (A227)

Location Lecce

Calculus. Probability theory. Linear Algebra. Programming skills.

The course provides a modern introduction to data mining, which spans techniques, algorithms and methodologies for discovering structure, patterns and relationships in data sets (typically, large ones) and making predictions. Applications of data mining are already happening all around us, and, when they are done well, sometimes they even go unnoticed. For instance, how does the Google web search work? How does Shazam recognize a song? How does Netflix recommend movies to its users? The principles of data mining provide answers to these and others questions. Data mining overlaps the fields of computer science, statistical machine learning and data bases. The course aims at providing the students with the knowldedge required to explore, analyze and leverage available data in order to turn the data into valuable and actionable information for a company, for instance, in order to facilitate a decision-making process.

The course describes methods and models for the analysis of large amounts of data. Students will gain a solid background with a broad spectrum of basic knowledge related to data mining:

  • the students will acquire the basic cognitive tools to think analytically, creatively and critically, and have the abstraction and problem-solving skills needed to cope with complex systems;
  • they will gain solid knowledge of data mining models and methodologies;
  • they will be able to work on large data collections, including heterogeneous and produced at high speed data, in order to carry out in-depth thematic analyses, drawing on this knowledge to improve the decision-making process.

Frontal lessons using slides made available to students and classroom exercises

Oral exam. During the exam the student is asked to illustrate theoretical topics in order to verify his/her knowledge and understanding of the selected topics. The student must demonstrate adequate knowledge and understanding of the issues presented or indicated, applying in a relevant manner the theories and conceptual models covered by the study programme.

Mining of Massive Datasets 

J. Leskovec, A. Rajaraman and J. Ullman

Freely availableonline: http://www.mmds.org

 

Data Mining and Analysis

M. J. Zaki and W. Meira

Freely available online: https://dataminingbook.info

(ING-INF/05)
DATA MINING

Degree course MATEMATICA

Subject area ING-INF/05

Course type Laurea Magistrale

Credits 6.0

Teaching hours Ore totali di attività frontale: 42.0

For matriculated on 2022/2023

Year taught 2022/2023

Course year 1

Semestre Secondo Semestre (dal 27/02/2023 al 09/06/2023)

Language INGLESE

Subject matter TEORICO-MODELLISTICO (A217)

Location Lecce

Calculus. Probability theory. Linear Algebra. Programming skills.

The course provides a modern introduction to data mining, which spans techniques, algorithms and methodologies for discovering structure, patterns and relationships in data sets (typically, large ones) and making predictions. Applications of data mining are already happening all around us, and, when they are done well, sometimes they even go unnoticed. For instance, how does the Google web search work? How does Shazam recognize a song? How does Netflix recommend movies to its users? The principles of data mining provide answers to these and others questions. Data mining overlaps the fields of computer science, statistical machine learning and data bases. The course aims at providing the students with the knowldedge required to explore, analyze and leverage available data in order to turn the data into valuable and actionable information for a company, for instance, in order to facilitate a decision-making process.

The course describes methods and models for the analysis of large amounts of data. Students will gain a solid background with a broad spectrum of basic knowledge related to data mining:

  • the students will acquire the basic cognitive tools to think analytically, creatively and critically, and have the abstraction and problem-solving skills needed to cope with complex systems;
  • they will gain solid knowledge of data mining models and methodologies;
  • they will be able to work on large data collections, including heterogeneous and produced at high speed data, in order to carry out in-depth thematic analyses, drawing on this knowledge to improve the decision-making process.

Frontal lessons using slides made available to students and classroom exercises

Oral exam. During the exam the student is asked to illustrate theoretical topics in order to verify his/her knowledge and understanding of the selected topics. The student must demonstrate adequate knowledge and understanding of the issues presented or indicated, applying in a relevant manner the theories and conceptual models covered by the study programme.

Mining of Massive Datasets 

J. Leskovec, A. Rajaraman and J. Ullman

Freely availableonline: http://www.mmds.org

 

Data Mining and Analysis

M. J. Zaki and W. Meira

Freely available online: https://dataminingbook.info

DATA MINING (ING-INF/05)
MOBILE APPLICATIONS DEVELOPMENT

Degree course DIGITAL HUMANITIES

Subject area ING-INF/05

Course type Laurea Magistrale

Credits 6.0

Teaching hours Ore totali di attività frontale: 42.0

For matriculated on 2022/2023

Year taught 2022/2023

Course year 1

Semestre Primo Semestre (dal 19/09/2022 al 13/01/2023)

Language INGLESE

Subject matter COMUNE/GENERICO (999)

There are no prerequisites for this course; no previous programming experience is required.

This course is an introduction to mobile application development for iOS and Android. Students will learn how to develop simple mobile applications for IOS and Android with Flutter, an open source cross-platform framework by Google.

The main objective of the course is to provide students with the skills needed to design and develop mobile applications.
During the classes they will learn to:
- think like a programmer;
- use the Dart language to write simple programs;
- write mobile applications through the Flutter framework;
- use an IDE (integrated development environment) for their development activities;
- read and take advantage of technical documentation.

- Classroom lectures and pratical exercises;
The course involves a hands-on approach to the topics covered, so class attendance is recommended.

Students will be required to present and discuss a mobile application they have designed based on an idea agreed upon with the lecturer.

They will be evaluated based on their knowledge of the Dart programming language and their ability to design and implement a mobile application using Flutter

Online manuals:
- Dart online documentation on https://dart.dev
- Flutter online documentation on https://flutter.dev

Books:
- Flutter Apprentice Learn to Build Cross-Platform Apps by Mike Katz et al.
- Beginning Flutter A hands on guide to app development by Marco L. Napoli

MOBILE APPLICATIONS DEVELOPMENT (ING-INF/05)
PARALLEL ALGORITHMS

Degree course COMPUTER ENGINEERING

Subject area ING-INF/05

Course type Laurea Magistrale

Credits 9.0

Owner professor Massimo CAFARO

Teaching hours Ore totali di attività frontale: 81.0

  Ore erogate dal docente MARCO PULIMENO: 27.0

For matriculated on 2021/2022

Year taught 2022/2023

Course year 2

Semestre Secondo Semestre (dal 01/03/2023 al 09/06/2023)

Language INGLESE

Subject matter PERCORSO COMUNE (999)

Location Lecce

Programming skills and working knowledge of the C programming language.

This is the practical MPI programming part of the Parallel Algorithms course https://www.unisalento.it/people/massimo.cafaro/didattica/1315572021/scheda.

It is based on a pragmatic approach to parallel programming of message-passing algorithms using the C language and the MPI library.

Applying knowledge and understanding. 

After this part of the course the student should be able to:·

· Design, implement and analyze message-passing based parallel algorithms in C using the MPI library;

· Describe and use basic parallel algorithms.

The practical part of the Parallel Algorithms course consists of classroom exercises. There will be lessons devoted to exercises in which we will illustrate, with plenty of examples, how the theoretical knowledge acquired can be used in order to solve algorithmic problems of practical interest and implement parallel algorithms in C language through the MPI library.

Parallel Programming in C with MPI and OpenMP (International Edition). Michael J. Quinn. McGraw-Hill

PARALLEL ALGORITHMS (ING-INF/05)