- Docente: ANDREA PANCINI
Piattaforma Kiro - Didattica Curriculare 3+2 e Lauree Magistrali Ciclo Unico
Risultati della ricerca: 1869
- Docente: MARCO GARDINI
- Docente: STEFANO FERRARI
- Docente: RAFAEL PENALOZA NYSSEN
- Docente: Luigi Fontana
- Docente: LUCA RONDI
- Docente: LOURENCO BEIRAO DA VEIGA
- Docente: FRANCO DASSI
- Docente: GIUSEPPE CHIRICO
- Docente: MARCO TOLIMAN LUCCHINI
- Docente: EMANUELA BRICOLO
- Docente: MARIA TERESA GUASTI
- Docente: GABRIELLA PASI
- Docente: ALESSANDRO RAGANATO
All lectures will be recorded and videos will be made available on this webpage.
Lectures
- Wednesday 13:15-16:00 (U5/2109)
- Thursday 13:15-15:00 (U5/2107)
First lecture: Wednesday 5 October 2022
Instructors
Tal Orenshtein, Emanuele Dolera
- Wednesday 13:15-16:00 (U5/2109)
- Docente: EMANUELE DOLERA
- Docente: TAL ORENSHTEIN
- Docente: Laura D'Angelo
- Docente: CLAUDIA CASELLATO
- Docente: MARCO CALAUTTI
- Docente: GABRIELLA PASI
- Docente: Zuzana Pinkosova
The course aims to provide students with the main concepts behind the management of data originating from the Web and social media, from collection to modeling, to subsequent analysis and search.
Students will be able, in particular, to collect and store data from the Social Web, either through the use of APIs or scraping, to use advanced representations (both topological and semantic), and to analyze and visualize complex network structures and related analyses. Part of the course will focus especially on the concepts of "Web search" and "social search" and the study of the most appropriate models and dimensions of relevance in the context of the Social Web.
- Docente: Marco Braga
- Docente: GEORGIOS PEIKOS
- Docente: MARCO VIVIANI
This course intends to teach the students a range of processing and analysis techniques commonly applied to signals in various contexts. The students will learn:
how to represent a deterministic signal in the frequency domain;
how to analyze deterministic signals and calculate fundamental properties (spectrum, power/energy);
the stability of linear systems;
how to design digital filters;
how implementing some studied methods in Matlab.
- Docente: ANNA VIZZIELLO
Through a series of lectures and case studies students will gain an understanding of challenges in using machine learning in health care and its application in medicine. The course will cover key use cases such as clinical decision support, personalized medicine and electronic phenotyping.
- Docente: ARIANNA DAGLIATI
- Docente: ROBERTO DE ICCO
- Docente: ANTONIO PISANI
- Docente: FEDERICO ANTONIO NICOLO AMEDEO CABITZA
- Docente: Chiara Natali
- Docente: SILVIA SERINO
- Docente: FRANCESCO ZANATTA