PhD courses 2020-21

Next courses (approved by the PhD Committee)

  • Course teacher: prof. Emilio Carrizosa, Universidad de Sevilla (Spain)
  • Course title: Mathematical Optimization in Machine Learning (course)
  • Specially suited for curricula: AOSC, INF,
  • Proposed teaching period: June 7,8,9,10,  2021
  •  CFU’s: 2 CFU, 8 hours
  • Organizer: Fabio Schoen

Mathematical Optimization in Machine Learning
Emilio Carrizosa,
University of Seville, Spain

Mathematical Optimization is at the core of many Machine Learning problems in classification, regression and dimensionality reduction, amon others. An important challenge is to make classification and prediction algorithms more interpretable, in the sense that we should know which attributes, and at which extent, contribute in the prediction.
Mathematical Optimization allows us to pose in a natural way the multiobjective problem of optimizing the performance and, at the same time, the number of attributes or measurement costs.

In this course we will illustrate the use of Mathematical Optimization strategies in different problems, such as dimensionality reduction (sparse PCA), sparse linear models with performance constraints, cost-sensitive Support Vector Machines with performance constraints or functional data, sparse classification and regression (ensembles of) trees, interpretable clustering, etc., with special focus on the methods developed by the research group in Optimization in IMUS, the Institute of Mathematics of the University of Seville.

  • Course teacher: Giuseppe Pelosi, Stefano Selleri
  • Course title: Finite Elements for Engineers
  • Specially suited for Curricula: EEE
  • Proposed teaching period: June 19, 20, 21, 2021
  • CFU’s: 2 (8 hours)

Abstract

The course will present the finite element method (FEM) starting from its storical
development and general impact in engineering.

It will then present the classes of problems which can be solved with it, presenting
several examples, spanning from electric machines examples, to high frequency electromagnetics
and thermal or civil engineering.

Part of the course is devoted to advanced techniques for speeding up computation.

 
  • Course teacher: Stefano Maddio
  • Course title: Indoor Wireless Positioning
     
  • Specially suited for Curricula: EEE, TLC
  • Proposed teaching period: postponed to June/July 2021
  • CFU’s: 2 (8 hours)

Abstract:

La consapevolezza spaziale è la chiave per un nuovo mondo di applicazioni nel mondo della comunicazioni personali: navigazione museale, tracciamento dei pazienti, guida per ipo-vedenti e molte altre ancora.

Negli ambienti al chiuso, i sistemi basati sul GPS non sempre sono efficaci per questo fine. L’errore del sistema GPS può essere incompatibile con la scala spaziale richiesta dall’applicazione.

Un’alternativa all’uso del GPS consiste nel utilizzare le comunicazioni wireless, oramai onnipresenti in ambienti indoor. Con lo stesso apparato con cui vengono scambiati dati e infatti possibile stimare la posizione di dispositivi mobili, senza la necessità di sensoristica aggiuntiva.

Questo corso si propone di trattare i sistemi di localizzazione indoor basati su sistemi di smart antennas applicato ai protocolli di comunicazione esistenti.

  • Course teacher: David Angeli, DINFO, Unifi
  • Course title: Fundamentals of Economic Model Predictive Control
  • Specially suited for Curricula: AOSC
  • Proposed teaching period: July 2021
  • CFU’s: 1 (4 hours)
  • Course teacher: Alessandro Fantechi
  • Course title: Guidelines for software development in safety-critical domains: examples from different transportation domains
  • Specially suited for Curricula: AOSC, INF
  • Proposed teaching period: June, 2021
  • CFU’s: 2 (8 hours)

Abstract

The course will introduce the guidelines adopted in different transportation domains (automotive, railway, avionics,…) that rule the software development with the aim of minimising the presence of software faults (aka “bigs”) that can threat the safety of the users of the transportation systems in which the software is embedded. It will be shown how the examined guidelines reflect different safety cultures related to the domains of interest.
  • Course teacher: Graziano Chesi, Hong Kong University
  • Course title: An introduction to SOS programming with applications in dynamical systems (course)
  • Specially suited for Curricula:AOSC, INF
  • Proposed teaching period: June 26- July 7, 2021
  • CFU’s: 2 (8 hours)
  • Course teacher: prof. Sotirios A. Tsaftaris (Univ. Edinburgh)
  • Course titles:  #1: Fundamental s of representation learning and disentangled representations
    #2: Learning disentangled representations for applications
    in computer vision, healthcare and text: common designs,
    challenges and opportunities
  • Specially suited for Curricula: INF
  • Proposed teaching period: October 2021
  • CFU’s:  1 (4 hours)
  • Organizer: prof. Carlo Colombo
  • Course teacher: Franco Bagnoli, Unifi
  • Course title: Introduction to statistical physics from a computational perspective (course)
  • Specially suited for Curricula: AOSC
  • Proposed teaching period: Any period
  • CFU’s: 4 (16 hours)

Prof. Giovanni Toso (European Space Agency):

2 seminars (2+2 hours, 2 CFU):

  • Multibeam antennas for Satellite Communications
  • Active Antennas for Satellite Communications

for information: prof. Stefano Selleri

  • Course teacher: Ronald Tetzlaff – Alon Ascoli, Technische Universitãt Dresde
  • Course title: Recent trends in memristors theory and applications (course)
  • Specially suited for Curricula: AOSC
  • Proposed teaching period: March-May 2021
  • CFU’s: 2 (8 hours)
  • Course teacher: Matthew O’Donnell, Ph.D., University Washington
  • Course title: Light and Sound: Integrating Photonics with Ultrasonics (Seminar)
  • Specially suited for Curricula:EEE
  • Proposed teaching period: 15/12/2020
  • CFU’s: 1 (1hours)

https://phd.dii.unipi.it/en/courses/item/3620-prof-luigi-chisci,-università-degli-studi-di-firenze-italy-linear-and-nonlinear-kalman-filtering-theory-and-applications-,-12,14,19,21-january-2021.html

January 12, 2021 –  9.30 – 12.45
January 14 2021 – 9.30 – 12.45
January 19, 2021 – 9.30 – 12.45
January 21, 2021 – 9.30 – 12.45

Proposed CFU: 4 (16 hours)

This course is offered to a PhD program in Pisa, but might be useful for students of our PhD, mainly for  cycle XXXVI students or for students who did not follow prof. Chisci course held in the last academic year

Registration before February 4th, 2021

 
  • Course teacher: DINFO, KTH Royal Institute of Technology
  • Course title: Bioradar for Medical Applications (course)
  • Specially suited for Curricula: AOSC,EEE,INF,TLC
  • Proposed teaching period: Feb 10, 2021
  • CFU’s: 1 (6 hours)
  • Course teacher: Stefano Ricci, Enrico Boni
  • Course title: Electronics Embedded Systems for time critical applications (course)
  • Specially suited for Curricula: EEE
  • Proposed teaching period: Februart 15,16,17, 2021
  • CFU’s: 3 (12 hours)

Modern Electronics Embedded Systems represent compact, low-cost, power-efficient, programmable electronics systems. They support a very wide range of applications in the fields of motion control, IoT, home and industrial automation, sensing, etc. The heart of such systems is typically a micro-controller (uC). This course shows how to exploit uC together with its rich set of peripherals, like ADC, DMA, complex timers, power control, etc. Particular attention will be devoted to time-critical applications, where the uC is supposed to react in few us. The course is based on Laboratory activity. Students and tutors will develop together example applications on developer’s boards.

  • Course teacher: Marco Bertini, Lorenzo Seidenari, Tiberio Uricchio, DINFO, Unifi
  • Course title: Computer Vision and Deep Learning in Practice (course)
  • Specially suited for Curricula: EEE,TLC
  • Proposed teaching period: February 22, 2021
  • CFU’s: 4 (16 hours) 
  • COurse Title: Reinforcement Learning Virtual School – https://rlvs.aniti.fr/
  • Registration: before March 1, 2021
  • Course teachers: 
    Donald A. Berry University of Texas & Rice University
    Marta Garnelo DeepMind & Imperial College London
    Matthieu Geist Google Brain
    Leslie Kaelbling MIT
    Tor Lattimore DeepMind
    Jean-Baptiste Mouret Inria
    Matteo Pirotta Facebook AI Research
    Doina Precup McGill University & DeepMind
    Emmanuel Rachelson ISAE-SUPAERO, Université de Toulouse
    Antonin Raffin German Aerospace Center
    Olivier Sigaud Sorbonne Université
    Mengdi Wang Princeton University
    Dennis Wilson ISAE-Supaero, University of Toulouse
  • SPecially suited for Curricula: AOSC, INF
  • CFU’s: 12 (48 hours)
  • Period: 25/III – 9/IV/2021
Accordion Content
  • Course teacher: Manfred Jaeger, Aalborg University
  • Course title: Probabilistic Graphical Models (course)
  • Specially suited for Curricula: INF, AOSC
  • Proposed teaching period: 27-29/10, 3-5/11/2020
  • CFU’s: 3 (12 hours)
  • Course teacher: Dr. Andrei Zhuravlev, Russian Academy of Science
  • Course title: Development of a high-performance and compact microwave system for personnel screening on the move, designed for mass use (course)
  • Specially suited for Curricula: EEE
  • Proposed teaching period: 04/11/2020
  • CFU’s: 1 (3 hours)
  • Course teacher: Dr. Andrei Zhuravlev, Moscow State University
  • Course title: On the possible use of synthetic aperture radar for non-contact testing of rails rolling surface and rail joints – (course)
  • Specially suited for Curricula: EEE
  • Proposed teaching period: 05/11/2020
  • CFU’s: 1 (3 hours)
  • Course teacher: Dr. David Root, Keysigth Tech., USA
  • Course title: Microwave Enabled Quantum Computation (Seminar)
  • Specially suited for Curricula: EEE
  • Proposed teaching period: 11/11/2020
  • CFU’s: 1 (1 hours)
  • Course teacher: Dr. David Root, Keysigth Tech., USA
  • Course title: Quantum Algorithms: A First Look (Seminar)
  • Specially suited for Curricula: EEE
  • Proposed teaching period: 10/11/2020
  • CFU’s: 1 (1 hours)
  • Course teacher: Dr. Lesya Anishchenko,  Bauman Moscow State University
  • Course title: Bioradar for Medical Applications (course)
  • Specially suited for Curricula: EEE
  • Proposed teaching period: 03/11/2020
  • CFU’s: 1 (3 hours)
  • Course teacher: Massimiliano Pierobon, University of Nebraska-Lincoln (USA)
  • Course title: Molecular Communications to interface
    biological and electrical computing
  • Specially suited for Curricula:  AOSC, INF, EEE, TLC
  • Teaching period: November 24,25, 2020
  • proposed CFU’s: 1
  • Course teacher: Massimo Bombino, Riccardo Bernardini,Tullio Vardanega,  Software Sicuro srl, Univ of Udine, Univ of Padova
  • Course title: Reliable Sofftware Design and Production
  • Specially suited for Curricula: AOSC, INF, TLC
  • Proposed teaching period:Dec 15-17, 2020
  • CFU’s: 5 (21 hours)
 

AIDA course

Graph Neural Networks and Neural-Symbolic Computation

Description: This is an introductory course to the theory and applications of Graph Neural Networks (GNN) and to related topics in Neural-Symbolic Computation. The course gives the foundations on neural computation involving patterns represented by graphs in fields ranging from computer vision to bioinformatics. In addition, GNN will be presented for different applications in the case of graph-based domains, where inferential processes are expected to involve also the neighbors of vertexes (e.g. social networks). Finally, the diffusion mechanisms taking place by GNN will be integrated with more general Neural-Symbolic models where the decision mechanisms need to be coherent with external representations of environmental knowledge.

  1.  9:00 – 12:00, 2/4 – Neural computation on directed graphs, Diffusion
    on graphs, GNN
  2. 9:00 – 12:00, 2/11 – Convolution on graphs, lab experiments
  3. 9:00 – 12:00, 2/18 – Neuro-symbolic computation
  4. 9:00 – 11:00, 2/25 – Lab experiments
  5. 11:00 – 12:00, 2/25 – Seminar by Petar Veličković, Deep Mind

Institution: Université Côte d’Azur

Short Course

ECTS: N/A

Level: Master 2

Semester: Spring

Course start, Duration: Starts on Feb. 4, ends on Mar. 4, 5 weeks, Thursdays 9-12

Language: English

Participation mode: Zoom

Lecturers: Professor Marco Gori, MAASAI, Université Côté d’Azur and SAILab, University of Siena
Course assistance and seminars:

  • Dr. Petar Veličković, Deep Mind
  • Dr. Michelangelo Diligenti, Google and SAILab, University of Siena
  • Dr. Giuseppe Marra, KU Leuven
  • Matteo Tiezzi, SAILab, University of Siena

For registration, please contact Lucile Sassatelli lucile.sassatelli@univ-cotedazur.fr

Link to course: http://web.univ-cotedazur.fr/en/idex/formations-idex/data-science/

AIDA course

Registration: Interested students should enter the course webpage to register.

Machine Learning and Deep Neural Networks

Description: Introduction to Machine Learning, Artificial Neural Networks, Perceptron, Multilayer perceptron. Backpropagation, Deep neural networks. Convolutional NNs, Deep learning for object detection, Deep Semantic Image Segmentation, Generative Adversarial Networks, Recurrent Neural Networks. LSTMs, Data Clustering, Decision Surfaces. Support Vector Machines, Distance-based Classification, Dimensionality Reduction, Kernel Methods, Bayesian Learning, Deep Reinforcement Learning, CVML Software Development Tools

Institution: Aristotle University of Thessaloniki

Department: Department of Informatics

Short Course

ECTS: 1.5

Level: MSc/Senior undergraduate

Semester: Spring Semester

Start Day, Duration: 17/2/2021, 2 Days

Language: English

Participation mode: teleconference/tele-exams

Participation terms: See course website

Lecturer: Prof. Ioannis Pitas, pitas@csd.auth.gr

Link to course: https://icarus.csd.auth.gr/cvml-short-course-machine-learning-and-deep-neural-networks/

AIDA course:

Computer Vision and Image Processing

Description:

  • Image Processing: Introduction to Image Processing and Computer Vision, Image Formation, Image Sampling, 2D Systems, Image Transforms, Fast 2D Convolution Algorithms, Image Perception, Image Filtering.  
  • Computer Vision: Edge Detection, Region Segmentation, Texture Description, Shape Description, Image Acquisition, Camera Geometry, Stereo and Multiview Imaging, Structure from Motion, 3D Robot Localization and Mapping, Object Tracking.

Institution: Aristotle University of Thessaloniki

Department: Department of Informatics

Short Course

ECTS: 1.5

Level: MSc/Senior undergraduate

Semester: Spring Semester

Start Day, Duration: 24/2/2021, 2 Days

Language: English

Participation mode: teleconference/tele-exams

Participation terms: See course website

Lecturer: Prof. Ioannis Pitas, pitas@csd.auth.gr

Link to course: https://icarus.csd.auth.gr/cvml-short-course-computer-vision-image-processing/

Soft skill courses are available from the official page of the PhD programs at Unifi

We remind that it is necessary that every students gets CFU’s from these courses

PhD courses, subject to approval by the PhD Committee

Course: Sequence and graph learning

CFU: 3

Locatione: webex, (email paolo.frasconi@pm.me to get an invitation)

Course: Explainable AI

Dates: April 15, 22, 29​, 2021, 16-19.15

CFU: 3

Location: webex, (email paolo.frasconi@pm.me to get an invitation)

Official calendar of the PhD Program