Professors A.Y. 2025-2026
Professors
CASINI MARCO
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Code 2016823 -
[2016823] SYSTEM IDENTIFICATION -
Credit(s): 6
More information and training material
More info about the course are available in the web page: https://control.dii.unisi.it/sysid/
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Code 2017432 -
[2017432] SYSTEM IDENTIFICATION AND DATA ANALYSIS -
Credit(s): 12
More information and training material
More info about the course are available in the web pages:
http://www.dii.unisi.it/~control/sysid (**System Identification**)
http://www.dii.unisi.it/~control/sef (**State Estimation and Filtering**)
FALASCHI MORENO
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Code 2000207 -
[2000207] MODELS AND LANGUAGES FOR BIOINFORMATICS -
Credit(s): 6
More information and training material
Bioinformatics is a rapidly developing research field which is fundamental for the future development of biotechnologies. Bioinformatics research is relevant for the territory of Siena and Tuscany, in which there are several major industries which have a bioinformatic unity. Bioinformatic research is currently under a quick development all over the world.
GARULLI ANDREA
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Code 2017431 -
[2017431] STATE ESTIMATION AND FILTERING -
Credit(s): 6
More information and training material
More info about the course are available in the web page:
http://www.dii.unisi.it/~control/sef -
Code 2017432 -
[2017432] SYSTEM IDENTIFICATION AND DATA ANALYSIS -
Credit(s): 12
More information and training material
More info about the course are available in the web pages:
http://www.dii.unisi.it/~control/sysid (**System Identification**)
http://www.dii.unisi.it/~control/sef (**State Estimation and Filtering**)
GIORGI ROBERTO
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Code 109156D -
[109156D] HIGH PERFORMANCE COMPUTER ARCHITECTURE -
Credit(s): 9
More information and training material
OTHER INFORMATION: 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 Office hours are each Monday 16:30-18:30 or on appointment (giorgi@unisi.it), either in room 110 (S. Niccolò) or via telco.
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 classroom at https://classroom.google.com/c/ODAyNDMxNzY2NDc2 class code: rkkz7zxp
- 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/c/ODAyNDMxNzY2NDc2 class code: rkkz7zxp
MAGGINI MARCO
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Code 2013578 -
[2013578] BIG DATA -
Credit(s): 6
More information and training material
Relevant material is available on the course page in unisi moodle platform.
MECOCCI ALESSANDRO
- Code 2017429 - [2017429] DIGITAL IMAGE PROCESSING - Credit(s): 6
MELACCI STEFANO
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Code 2014376 -
[2014376] NEURAL NETWORKS -
Credit(s): 6
More information and training material
Lecture notes, software, examples are available in the course webpage on the USienaIntegra platform, https://elearning.unisi.it/course/view.php?id=13399 In order to access the material please contact the instructor.
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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/c/ODAyNDMxNzY2NDc2 class code: rkkz7zxp
MOCENNI CHIARA
- Code 2017398 - [2017398] COMPLEX SYSTEMS - Credit(s): 6
PAOLETTI SIMONE
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Code 109151D -
[109151D] DISCRETE-EVENT SYSTEMS -
Credit(s): 6
More information and training material
Lecture notes, video recordings, past written exams and exercises with solutions, and software are available on the Moodle space of the course: https://elearning.unisi.it/course/view.php?id=13552
PRANZO MARCO
- Code 109161D - [109161D] NETWORK OPTIMIZATION - Credit(s): 6
PRATTICHIZZO DOMENICO
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Code 2000184 -
[2000184] HUMAN-CENTERED ROBOTICS -
Credit(s): 6
More information and training material
Lecture notes and additional reading materials will be made available through the e-learning platform of the University.
SALVIETTI GIONATA
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Code 2016830 -
[2016830] INDUSTRIAL ROBOTICS -
Credit(s): 6
More information and training material
Numerous tools for simulating industrial robots will be made available.
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/HomePage.html (click on Teaching activity) Video-presentation of the 6EC module (LM-40): https://www.youtube.com/watch?v=vmysauk1Pnw -Disclaimer (for ERASMUS students) Erasmus students are welcome. Erasmus students are required to come through the exam FULLY positively in order to get their credits.
