Professors AY 2022-2023
Professors
ADDABBO TOMMASO
- Code 2015255 - [2015255] VIRTUAL INSTRUMENTATION AND DIGITAL EMBEDDED ELECTRONICS - Credit(s): 6
ALBANI MATTEO
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Code 109157D -
[109157D] MATHEMATICAL METHODS FOR ENGINEERING -
Credit(s): 6
More information and training material
6 CFU
BARTOLINI SANDRO
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Code 2000203 -
[2000203] DESIGN OF APPLICATIONS, SERVICES AND SYSTEMS -
Credit(s): 9
More information and training material
For any doubt, drop an email to the teacher.
- Code 2000493 - [2000493] DESIGN OF APPLICATIONS AND SERVICES - Credit(s): 6
- Code 2016226 - [2016226] INTRODUCTORY COURSE FOR MSC DEGREES - Credit(s): 0
BIANCHINI MONICA
- Code 2000189 - [2000189] BIOINFORMATICS - Credit(s): 6
CASINI MARCO
- Code 2000188 - [2000188] MULTIVARIABLE AND NONLINEAR CONTROL - Credit(s): 6
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Code 2000365 -
[2000365] SYSTEM IDENTIFICATION AND DATA ANALYSIS -
Credit(s): 9
More information and training material
Tutte le informazioni relative al corso e il materiale didattico sono disponibili nel sito:
http://www.dii.unisi.it/~control/siadaLink alle lezioni online: https://meet.google.com/rag-gqmh-ijd
- Code 2000666 - [2000666] DATA ANALYSIS - Credit(s): 6
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Code 2001217 -
[2001217] MULTIVARIABLE, NONLINEAR AND ROBUST CONTROL -
Credit(s): 9
More information and training material
Website: http://control.dii.unisi.it/mnrc/
FALASCHI MORENO
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Code 2000207 -
[2000207] MODELS AND LANGUAGES FOR BIOINFORMATICS -
Credit(s): 6
More information and training material
Link per didattica a distanza:
https://meet.google.com/urn-hvzc-xbbLa bioinformatica è una disciplina scientifica emergente, il cui sviluppo è fondamentale per le biotecnologie del futuro. La ricerca in bioinformatica e’ rilevante per il territorio senese e toscano, in cui vi operano diverse grosse industrie biofarmaceutiche, che possiedono una unita’ bioinformatica. La ricerca in bioinformatica e’ in rapido sviluppo a livello internazionale.
FORT ADA
- Code 109163D - [109163D] SENSORS AND MICROSYSTEMS - Credit(s): 6
GARULLI ANDREA
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Code 2000365 -
[2000365] SYSTEM IDENTIFICATION AND DATA ANALYSIS -
Credit(s): 9
More information and training material
Tutte le informazioni relative al corso e il materiale didattico sono disponibili nel sito:
http://www.dii.unisi.it/~control/siadaLink alle lezioni online: https://meet.google.com/rag-gqmh-ijd
- Code 2000666 - [2000666] DATA ANALYSIS - Credit(s): 6
GIANNITRAPANI ANTONIO
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Code 2012411 -
[2012411] DECISION ANALYSIS -
Credit(s): 6
More information and training material
The course will be delivered in person. Videos covering all the topics of the course will be available in a shared Google drive.
Further information can be found at http://control.dii.unisi.it/ad/
- Code 2012411 - [2012411] DECISION ANALYSIS - Credit(s): 6
GIORGI ROBERTO
- Code 109156D - [109156D] HIGH PERFORMANCE COMPUTER ARCHITECTURE - Credit(s): 9
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Code 109156D -
[109156D] HIGH PERFORMANCE COMPUTER ARCHITECTURE -
Credit(s): 9
More information and training material
The teaching material is the reference text book and the lesson slides that are available on the site: https://hpca.diism.unisi.it/lezioni.htm
Suggestions for a better preparation: both in the written and oral exams (but also in the projects) the student is mainly required to show his/her detailed understanding of the topic, at least at the level shown by the teacher during the lesson. It's greatly appreciated the capacity of reasoning on the problem, rather then a mechanical (pedant) description of the topic.
In case of written exercise, we mainly look at the correctness of the solution (in terms of numbers) and a very short justification of the chosen way to carry out the exercise (lengthy general wording is completely unnecessary).
In case of oral question, the topic is typically one of the concepts illustrated during the lesson. Elements that are required are, for instance: the proof of the concept/theorem, precise schematic of the system, detailed behavior and functioning, reasons why this solution is used in the real-world.To follow the lessons online, refer to the most up-to-date information available on the course website https://hpca.diism.unisi.it/lezioni.htm
- Code 2015246 - [2015246] ADVANCED COMPUTER ARCHITECTURES - Credit(s): 6
GORI MARCO
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Code 2016821 -
[2016821] FUNDAMENTALS OF MACHINE LEARNING -
Credit(s): 6
More information and training material
Informazioni sul corso:
Webpage of the course on classroom athttps://classroom.google.com/u/0/c/NTU0Njg4NDUwNTI5
class code: 6zibd4t
- Code 2016821 - [2016821] FUNDAMENTALS OF MACHINE LEARNING - Credit(s): 6
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Code 2016822 -
[2016822] MACHINE LEARNING -
Credit(s): 12
More information and training material
Informazioni sul corso:
Webpage of the course on classroom at
https://classroom.google.com/u/0/c/NTU0Njg4NDUwNTI5 class code: 6zibd4t
MAGGINI MARCO
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Code 2000194 -
[2000194] LANGUAGE PROCESSING TECHNOLOGIES -
Credit(s): 6
More information and training material
The course material (lecture slides and example code) is available on the USienaIntegra (Moodle) site:
MALVEZZI MONICA
- Code 2015292 - [2015292] DIGITAL MODELLING, DESIGN AND MANUFACTURING - Credit(s): 6
MECOCCI ALESSANDRO
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Code 109146D -
[109146D] ADVANCED DIGITAL IMAGE PROCESSING -
Credit(s): 9
More information and training material
Nel caso le lezioni si tengano online saranno svolte utilizzando la piattaforma G-Meet. Il link per collegarsi alla room sarà riportato qui di seguito non appena disponibile: .......
Collegamenti utili
http://www.kluweronline.com/issn/0920-5691 http://www.kluweronline.com/issn/0920-5691
http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?puNumber=34 http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?puNumber=34
http://ieeexplore.ieee.org/xpl/conferences.jsp?letter=CVPR&findtitle=&sr... http://ieeexplore.ieee.org/xpl/conferences.jsp?letter=CVPR&findtitle=&sr... http://ieeexplore.ieee.org/ 49http://ieeexplore.ieee.org/
http://www.cs.ucf.edu/~vision http://www.cs.ucf.edu/~vision
http://www-2.cs.cmu.edu/afs/cs/project/cil/ftp/html/vision.html
http://www-2.cs.cmu.edu/afs/cs/project/cil/ftp/html/vision.html
http://www.cs.ucf.edu/courses/cap6411/cap5415_sp02.html http://www.cs.ucf.edu/courses/cap6411/cap5415_sp02.html
MELACCI STEFANO
- Code 2014376 - [2014376] NEURAL NETWORKS - Credit(s): 6
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Code 2014376 -
[2014376] NEURAL NETWORKS -
Credit(s): 6
More information and training material
Pagina del corso nella piattaforma USienaIntegra: https://elearning.unisi.it/course/view.php?id=6258
La pagina include materiale didattico e le informazioni su come vengono erogate le lezioni.
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Code 2014376 -
[2014376] NEURAL NETWORKS -
Credit(s): 6
More information and training material
Webpage of the course on classroom at
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Code 2016822 -
[2016822] MACHINE LEARNING -
Credit(s): 12
More information and training material
Informazioni sul corso:
Webpage of the course on classroom at
https://classroom.google.com/u/0/c/NTU0Njg4NDUwNTI5 class code: 6zibd4t
MOCENNI CHIARA
- Code 109149D - [109149D] COMPLEX DYNAMIC SYSTEMS - Credit(s): 6
PAOLETTI SIMONE
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Code 109151D -
[109151D] DISCRETE-EVENT SYSTEMS -
Credit(s): 6
More information and training material
Lectures of the course will be in presence only (no streaming). Videos covering the contents of the course will be made available to registered students. Information about the course (including instructions on how to register for the course) can be found at the web page: https://www3.diism.unisi.it/~paoletti/teaching/sed/2223/index.html
PRANZO MARCO
- Code 109161D - [109161D] NETWORK OPTIMIZATION - Credit(s): 6
PRATTICHIZZO DOMENICO
- Code 2000184 - [2000184] HUMAN-CENTERED ROBOTICS - Credit(s): 6
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Code 2000184 -
[2000184] HUMAN-CENTERED ROBOTICS -
Credit(s): 6
More information and training material
Bibliographic references will be provided during the course.
SCARSELLI FRANCO
- Code 2013578 - [2013578] BIG DATA - Credit(s): 6
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Code 2013578 -
[2013578] BIG DATA -
Credit(s): 6
More information and training material
More infomation will be available on home page of the course on unisi moodle platform.
TRENTIN EDMONDO
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Code 109148D -
[109148D] ARTIFICIAL INTELLIGENCE -
Credit(s): 9
More information and training material
See the official webpage at http://www.dii.unisi.it/~trentin/Teaching.html
Video-presentation of the 6EC module (LM-40): https://www.youtube.com/watch?v=vmysauk1Pnw
-Disclaimer (for ERASMUS students)
Erasmus students are welcome. Nonetheless, there are a few, major things that the perspective Erasmus student has to bear in mind in case she/he decides to attend this course:
1) There are NO DISCOUNTS for Erasmus students. All students are required to come through the exam FULLY positively in order to get their credits.
2) The exam is Italian style, meaning that several students usually fail their first attempts (because the exam is difficult) but they are allowed to come back for whole new examinations in the next MONTHS or even years. Erasmus students may have difficulties, thus, since they usually return to their home countries at the end of the semester (which prevents them from re-trying the exam). This implies that they need to be real sure they are willing to take the exam in the first place, and be real sure they are ready to go at 1st attempt.
3) Also, according to point (2), there will be NO EXTRA EXAMS scheduled in the days immediately next to the official exam date(s) just in order to allow Erasmus students to try again: preparing this exam takes time, no recovery from a complete failure can be seriously expected in less than a few (> 2) weeks time.
4) In case the Erasmus students return to their home countries without coming through the exam positively, the only way to get their credits is to come back to Siena for re-taking the exam entirely (in fact, the exam involves a mandatory oral part) on the next available official date that suits the students' needs (in this case, the students are required to communicate officially their intentions to re-try the exam during the Fall session to the proper Erasmus/International Relations Office of the University of Siena before leaving for the summer break, in order not to get their records removed from the system at the end of the Summer session). Different arrangements were attempted in the past, all resulting in endless troubles on both sides, such that they will no longer be applied.
5) FYI (it's better to know before you choose): historically, statistical outcomes from previous years show that MORE THAN 95% of the Erasmus students enrolled in this subject COULD NOT COME THROUGH the examination.
ZANVETTOR GIOVANNI GINO
- Code 2016226 - [2016226] INTRODUCTORY COURSE FOR MSC DEGREES - Credit(s): 0