Training

The students have to attend at least 140 hours of courses (overall in 3 years, the earlier the better).
Each PhD student is expected to:
  • attend and take the exam of three or more courses involving at least 80 hours of lectures in total. These courses should be selected among the ones made available by our PhD program and by the other 4 PhD programs of PhD-AI.it;
  • attend at least additional 60 hours of training activities provided exclusively for PhD students by the universities and research institutions of PhD-AI.it (or from other Italian and International institutions, subject to authorization of the PhD Board)
  • attend two PhD schools organized by PhD-AI.it (the corresponding hours are included in the 60 hours above)
The additional 60 hours of training activities may include:
  • cycles of seminars and doctoral schools, organized by university or research institutions external to PhD-AI.it (subject to the authorization of the PhD Board), with specific indication that they are aimed exclusively or mainly at doctoral students.
The additional 60 hours of training activities may include, up to a maximum of 20 hours:
  • activities on soft skills, research management, European and international research systems, entrepreneurship, intellectual property, etc., organized by the university or research institutions of the PhD-AI.it.
Below you’ll find a list of selected courses provided by the partner institutions.

Ph.D. Courses

Advanced Concepts in Network Theory I

Institution: Scuola IMT Lucca
Location: IMT School for Advanced Studies Lucca, p.zza San Francesco 19, 55100 Lucca (Italy)
Type: Ph.D. course
Attendance Mode: Blended
Exam: Yes
Lecturers: Tiziano Squartini
Email: tiziano.squartini@imtlucca.it
Academic Year: 2021/2022
Semester: 1
Hours: 20
Timetable: The timetable may be subject to change: please write to phd@imtlucca.it and ask to have the calendar shared.
Abstract: The first part of the course “Advanced Concepts in Network Theory” focuses on the topic of network reconstruction. Early attempts to infer missing information about networks will be reviewed, putting particular emphasis on the use of such techniques to reconstruct financial networks.

Advanced Concepts in Network Theory II

Institution: Scuola IMT Lucca
Location: IMT School for Advanced Studies Lucca, p.zza San Francesco 19, 55100 Lucca (Italy)
Type: Ph.D. course
Attendance Mode: Blended
Exam: Yes
Lecturers: Tiziano Squartini
Email: tiziano.squartini@imtlucca.it
Academic Year: 2021/2022
Semester: 1
Hours: 20
Timetable: The timetable may be subject to change: please write to phd@imtlucca.it and ask to have the calendar shared.
Abstract: The second part of the course “Advanced Concepts in Network Theory” focuses heavily on deeper theoretical aspects and their consequences. Particular emphasis will be put on maximum entropy models to study weighted complex networks.

Advanced Methods for Complex Systems I

Institution: Scuola IMT Lucca
Location: Lucca
Type: Ph.D. course
Attendance Mode: Blended
Exam: Yes
Lecturers: Diego Garlaschelli
Email: diego.garlaschelli@imtlucca.it
Academic Year: 2021/2022
Semester: 2
Hours: 20
Timetable: Scheduled 07/02/2022 – 18/02/2022 – check www.imtlucca.it for updates
Abstract: This interdisciplinary course aims at introducing rigorous tools from statistical physics, information theory and probability theory for investigating real-world complex systems arising in different fields of research. First, some key aspects of complexity encountered in physical, biological, social, economic and technological systems will be reviewed. Then, emphasis will be put on the construction of theoretical models based on the concept of constrained randomness, i.e. the maximisation of the entropy subject to suitable constraints. This will lead to the introduction of maximum-entropy models that serve as mathematical benchmarks for the properties of highly heterogeneous systems. Special cases of interest for this first part of the course include statistical ensembles of time series and correlation matrices with given properties. Applications to pattern detection in econophysics and neuroscience will be discussed. Full mathematical derivations of the models, as well as methods of statistical inference and model selection for data analysis will be provided. Lecture Contents. From Complexity to Thermodynamics. From Thermodynamics to Statistical Physics. Entropy in Probability Theory. Entropy in Information Theory. Empirical patterns in univariate time series. Empirical patterns in multivariate time series. Community detection for correlation matrices.

Advanced Methods for Complex Systems II

Institution: Scuola IMT Lucca
Location: Lucca
Type: Ph.D. course
Attendance Mode: Blended
Exam: Yes
Lecturers: Diego Garlaschelli
Email: diego.garlaschelli@imtlucca.it
Academic Year: 2021/2022
Semester: 2
Hours: 20
Timetable: Scheduled 02/05/2022 -16/05/2022. Check www.imtlucca.it for updates
Abstract: The second part of the course “Advanced Methods for Complex Systems” focuses on advanced practical applications of the concepts introduced in the first part. In particular, emphasis will be put on the successful areas of pattern detection and network modelling. Network pattern detection is the identification of robust empirical patterns (like scale-invariance, clustering, assortativity, reciprocity, motifs, etc.) that are widespread across real-world networks and that deviate systematically from some null hypothesis formalised in terms of a suitable random graph model. The models introduced in part 1 will then be used here for pattern detection purposes. Similarly, they will be used for modelling the properties of real networks in terms of explanatory factors.The course will include a combination of recent and ongoing research in the NETWORKS unit at IMT Lucca, thereby offering directions for possible PhD projects in this area. Lecture Contents. Complex networks: robust empirical properties. Maximum-entropy network ensembles. Networks with given degree sequence. Maximum likelihood parameter estimation in network ensembles. Pattern detection in networks. Reciprocity and the Reciprocal Configuration Model. The International Trade Network (econometric vs network modelling).

Advanced Topics in Econometrics

Institution: Scuola IMT Lucca
Location: IMT Lucca
Type: Ph.D. course
Attendance Mode: Blended
Exam: Yes
Lecturers: Francesco Serti
Email: francesco.serti@imtlucca.it
Academic Year: 2021/2022
Semester: 2
Hours: 20
Timetable: 8,9,10,16,29 Giugno + 1,5 Luglio
Abstract: This module covers some of the most important methodological issues arising in any field of applied economics when the main scope of the analysis is to estimate causal effects. A variety of methods are illustrated using theory and papers drawn from the recent applied literature

Advanced Topics in Machine Learning

Institution: Scuola IMT Lucca
Location: Lucca, Piazza S. Francesco 19, IMT School for Advanced Studies, Classroom to be defined
Type: Ph.D. course
Attendance Mode: Blended
Exam: Yes
Lecturers: Giorgio Gnecco
Email: giorgio.gnecco@imtlucca.it
Academic Year: 2021/2022
Semester: 2
Hours: 10
Timetable: Lectures currently scheduled in the following days: 14/9/2022 (4pm-6pm), 19/9/2022 (11am-1pm), 21/9/2022 (4pm-6pm), 27/9/2022 (11am-1pm), 7/10/2022 (11am-1pm)
Abstract: The course provides an introduction to the theory behind some advanced machine learning techniques, including some topics of recent research. MATLAB implementations of most of the techniques examined in the course are described in the related course “MATLAB for Data Science”.

AI Ethics Today

Institution: Università di Trento
Location: Trento, via Sommarive 9, Polo Scientifico Fabio Ferrari (Povo 1)
Type: Ph.D. course
Attendance Mode: In person
Exam: Yes
Lecturers: Prof. James Brusseau
Email: iecs.school@unitn.it
Academic Year: 2021/2022
Semester: 1
Hours: 20
Timetable: from March 7, 2022 to March 18, 2022
Abstract: The ethical elements of artificial intelligence will be located and discussed, and students will become proficient in forming their own strong arguments.

The course will center on nine ethical principles commonly employed in today’s AI ethics:
Individual ethics: Autonomy, Human Dignity, Privacy
Social ethics: Fairness, Solidarity, Sustainability
Technical ethics: Performance, Safety, Accountability
By analyzing and discussing case studies, students will learn how the principles are being applied today in academic and real-world discussions. Also, some topics in the contemporary philosophy of artificial intelligence will be briefly considered.

Algebra of tensors and applications

Institution: Università di Siena
Location: DIISM, San Niccolò Building, University of Siena, Siena
Type: Ph.D. course
Attendance Mode: In person
Exam: Yes
Lecturers: Luke Oeding
Email: oeding@auburn.edu
Academic Year: 2021/2022
Semester: 2
Hours: 20
Timetable: May 2-6, 2022
Abstract: Tensor calculus has many applications in several recent fields related to information processing, information theory and computer science. For instance it provides the basis for the construction of efficient algorithms in deep learning, artificial intelligence, computer vision, quantum information, etc. The goal of the course is to teach students fundamental methods for tensor calculus, based on the use of symmetries. The course will illustrate how symmetries provide the tools for the use of computational cluster resources to do massive computations in parallel. Then students will be able to approach questions like Computations with Neural Networks, or Separation of Entanglement Classes, etc.

Analysis of Survey data and Small Area Estimation

Institution: Università di Pisa
Location: Pisa, Polo didattico Le Piagge, room to be defined
Type: MSc course
Attendance Mode: Blended
Exam: Yes
Lecturers: Monica Pratesi – Francesco Schirripa Spagnolo
Email: francesco.schirripa@ec.unipi.it
Academic Year: 2021/2022
Semester: 2
Hours: 42
Timetable: Lectures will be held every week in the semester 2 on:

– Monday, 12:15 – 13:45 (Teams)

– Thursday, 12:15 – 13:45 (Teams)

– Friday, 12:15 – 13:45 (Teams)

Abstract: The course offers a review of the main Small Area Estimation Methods and teach how to apply them to European survey data to have a local monitoring of the Sustainable Development Goals and to estimate deprivation and inequality indicators, focusing also in aggregating Multidimensional Indicators and on conceptualizing, defining and measuring poverty under the capability approach. The course will be structured in the following parts

1) Analysis of the collected data for estimation and testing for the phenomenon under study; definition of planned and unplanned domains.

2) Direct and indirect estimates for unplanned domains; R codes for the application of the SAE estimators (EURAREA and SAMPLE project libraries)

3) quality issues in SAE and usage of SAE in European Statistical System.

At the end of the module student will be able to deal with small area estimation both at the theoretical and empirical level and to apply aggregation methods to indicators from the European survey data.

Students will learn the fundamental small area methods and what might be the problems that arise in the application of them and in the definition of their statistical quality.

Answer Set Programming

Institution: Università dell’Aquila
Location: L’Aquila, Coppito, Edificio Turing, A1.10
Type: Ph.D. course
Attendance Mode: Blended
Exam: Yes
Lecturers: Stefania Costantini and Abeer Dyoub
Email: stefania.costantini@univaq.it
Academic Year: 2021/2022
Semester: 2
Hours: 12
Timetable: To be defined
Abstract: Answer set programming (ASP) is a form of declarative programming oriented towards difficult search problems. ASP is an outgrowth of research on the use of nonmonotonic reasoning in knowledge representation and reasoning. It is particularly useful in knowledge-intensive applications, and is widely adopted also in industrial settings. ASP in fact offers a fully declarative, intuitive language for modelling and problem encoding, and efficient highly optimized inference engines (“solvers”) which are freely available. The course covers both theory and practice, proposing significant case-studies.

Applied Statistics (AS)

Institution: Scuola Superiore Sant’Anna
Location: Piazza Martiri della Libertà, 33 56127 Pisa (Italia)
Type: MSc course
Attendance Mode: Blended
Exam: Yes
Lecturers: Chiara Seghieri, Gaia Bertarelli
Email: chiara.seghieri@santannapisa.it
Academic Year: 2021/2022
Semester: 2
Hours: 20
Timetable: https://github.com/EMbeDS-education/StatsAndComputing20212022/wiki
Abstract: This course provides a review of the elements of statistical inference, as applied to realistic problems and data. It integrates a review of statistical theory with practice in data processing and analysis using STATA and R Software. Compared to traditional courses on Statistics, this course provides a practice-oriented approach to learning with a focus on interpretation of statistical results. The content will include topics selected from the following areas: 2) Planning a quantitative research study; 2) Fundamentals of Inference; 3) Point estimate, 4) Confidence Intervals and Hypothesis Testing; 5) Linear regression; 6) Introduction to the Generalized Linear Model with binary outcome. Prerequisites: A working knowledge of probability and descriptive statistics.

Approximate Bayesian Computation

Institution: Università di Firenze
Location: Firenze, Viale Morgagni 59, dipartimento di Statistica, Informatica, Applicazioni
Type: Ph.D. course
Attendance Mode: Blended
Exam: Yes
Lecturers: Fabio Corradi, Cecilia Viscardi
Email: fabio.corradi@unifi.it
Academic Year: 2021/2022
Semester: 2
Hours: 15
Timetable: June/July 2022
Abstract: ABC as an explanation of how Bayes rule works. Generative models.
ABC with no approximation. Examples from network analysis and Population genetics.
Statistics and approximations in ABC. Rejection ABC and its convergence to exact Bayesian
computation. Some limits in the use of Rejection ABC by examples. Further topics: Trade-off
between degree of approximation and computational efficiency. Relevance of the prior
distribution for mixing. Markov Chain Monte Carlo-ABC. Sequential methods: Population
MC and Sequential MC.
At the end of the course we provide an introduction to some more advanced topics like
Random Forest ABC, Selection of Statistics and Regression adjustment to be further
developed by a presentation given by the students in the last lecture.

Basic Linear Algebra and Statistics for Neuroscience

Institution: Scuola IMT Lucca
Location: Lucca, Piazza S. Francesco 19, IMT School for Advanced Studies, Classroom to be scheduled
Type: Ph.D. course
Attendance Mode: Blended
Exam: Yes
Lecturers: Giorgio Stefano Gnecco and Francesco Serti
Email: giorgio.gnecco@imtlucca.it
Academic Year: 2021/2022
Semester: 1
Hours: 30
Timetable: 17/11/2021: 16-18; 22/11/2021: 9-11; 24/11/2021: 9-11; 29/11/2021: 9-11; 1/12/2021: 9-11; 2/2/2022: 9-11; 3/2/2022: 9-12; 4/2/2022: 9-11; 7/2/2022: 14-16; 8/2/2022: 14-16; 11/2/2022: 14-16; 23/2/2022: 9-11; 24/2/2022: 14-16; 25/2/2022: 16-19
Abstract: Linear Algebra part (Gnecco, 10 hours): This course provides a basic introduction to linear algebra to students with no (or minimal) background on it. The emphasis is on the description of some applications of linear algebra, including some of interest to students in neuroscience, such as basic image processing, principal component analysis, and spectral clustering. More advanced material will be provided upon request to students having already a solid bakground in linear algebra.

Statistics part (Serti, 20 hours): This part of the course will provide students with an introduction to probability and statistics and it will be focused on topics that are particularly relevant to neuroscience. The lessons will be designed for students with a minimum knowledge of the subject.

Bayesian methods for high-dimensional data

Institution: Università di Firenze
Location: Florence, viale Morgagni 59 50134, Department of Statistics, Computer Science, Applications
Type: Ph.D. course
Attendance Mode: Blended
Exam: Yes
Lecturers: Francesco Stingo
Email: francesco.stingo@unifi.it
Academic Year: 2021/2022
Semester: 2
Hours: 10
Timetable: May 2022
Abstract: Bayesian approaches for model selection and inference in the context of: Linear regression,
GLM, Semi-parametric regression and other topics (e.g., mixtures, graphical models).
With applications in bio-medicine, with a particular focus on genomics.

Big Data Ethics

Institution: Università di Pisa
Location: Officine Garibaldi, via Gioderti 39, Pisa
Type: Post graduate Master course
Attendance Mode: Blended
Exam: No
Lecturers: Anna Monreale, Salvatore Ruggieri, Giovanni Comandè
Email: anna.monreale@unipi.it
Academic Year: 2021/2022
Semester: 2
Hours: 22
Timetable: TBA
Abstract: The module aims to introduce ethical and legal notions of privacy, anonymity, transparency and non-discrimination, also referring the Directives and Regulations of the European Union and their ongoing evolution. The module will show technologies for Privacy-by-Design, for predictive model auditing and for protecting the users’ rights and that allow the analysis of Big Data without harming the right to the protection of personal data, to transparency and to a fair treatment.

Big data sources, crowdsourcing, crowdsensing

Institution: Università di Pisa
Location: Officine Garibaldi, via Gioderti 39, Pisa
Type: Post graduate Master course
Attendance Mode: Blended
Exam: No
Lecturers: Tiziano Fagni
Email: tiziano.fagni@iit.cnr.it
Academic Year: 2021/2022
Semester: 2
Hours: 20
Timetable: TBA
Abstract: This module presentes techniques and methods for acquisition of Big Data from a large sources of data available, including mobile phone data, GPS data, customer purchase data, social network data, open and administrative data, environmental and personal sensor data. We discuss also several participatory methods for crowdsourcing or crowdsensing collection of data through ad hoc campains like serious games and viral diffusion.

Bioinformatics

Institution: Scuola Normale Superiore
Location: Pisa, piazza dei Cavalieri 7
Type: MSc course
Attendance Mode: Blended
Exam: Yes
Lecturers: Francesco Raimondi
Email: francesco.raimondi@sns.it
Academic Year: 2021/2022
Semester: 2
Hours: 40
Timetable: February – April
Abstract: Aim of the course is to provide students with the basic knowledge of bioinformatics techniques as an easy and friendly support for their study and research careers. This will entail: 1) theory of the most common bioinformatics algorithms and resources: who they are, what they do and why they are so important and increasingly used in modern biology research; 2) basic practical experience through hands-on-sessions on typical problems that can be answered by using popular online tools.

Bioinformatics

Institution: Università di Pisa
Location: Polo Fibonacci, University of Pisa
Type: MSc course
Attendance Mode: Blended
Exam: Yes
Lecturers: Nadia Pisanti
Email: nadia.pisanti@unipi.it
Academic Year: 2021/2022
Semester: 2
Hours: 48
Timetable: https://www.di.unipi.it/en/education/mcs/timetable-wif
Abstract: This course has the goal to give the student an overview of algorithmic methods that have been conceived for the analysis of genomic sequences. We will focus both on theoretical and combinatorial aspects as well as on practical issues such as whole genomes sequencing, sequences alignments, the inference of repeated patterns and of long approximated repetitions, the computation of genomic distances, and several biologically relevant problems for the management and investigation of genomic data. The exam has the goal to evaluate the students understanding of the problems and the methods described in the course. Moreover, the exam is additionally meant as a chance to learn how a scientific paper is like, and how to make an oral presentation on scientific/technical topics, that is designed for a specific audience.

Brain Networks

Institution: Scuola IMT Lucca
Location: Lucca, Piazza San Francesco 19, S Francesco Complex
Type: Ph.D. course
Attendance Mode: Blended
Exam: Yes
Lecturers: Tommaso Gili
Email: tommaso.gili@imtlucca.it
Academic Year: 2021/2022
Semester: 2
Hours: 10
Timetable: Expected 11-27 October 20221
Abstract: We shall provide the tools to measure and analyze the different kinds of networks that can be defined when studying the human brain (e.g. the functional and the structural one).

Bringing Perception to Social Robots: An introductory course

Institution: Università di Trento
Location: Trento, via Sommarive 9, Polo Scientifico Fabio Ferrari (Povo q), Garda room
Type: Ph.D. course
Attendance Mode: In person
Exam: Yes
Lecturers: Paolo Rota, Yiming Wang
Email: iecs.school@unitn.it
Academic Year: 2021/2022
Semester: 2
Hours: 20
Timetable: May 9, 2022: 1.30 p.m. – 5.30 p.m.
May 10, 2022: 1.30 p.m. – 5.30 p.m.
May 11, 2022: 1.30 p.m. – 5.30 p.m.
May 12, 2022: 1.30 p.m. – 5.30 p.m.
May 13, 2022: 1.30 p.m. – 5.30 p.m.
Abstract: In the past few years, social robots have been introduced into public spaces, such as museums, airports, commercial malls, banks, company showrooms, hospitals, and retirement homes, to mention a few examples. In addition to classical robotic skills with physical interactions, such as navigation, grasping and manipulating objects, social robots should be able to perceive and communicate with people in the most natural way, i.e. cognitive interactions. Visual perception is a stepping step for social robots to achieve such natural social interactions.

In this course, we will provide an introduction about socially aware robotics and the recent advances in deep learning with a particular coverage on its application on the most related vision tasks, e.g. face detection, facial landmark localisation and soft bio analysis. Following that, we will introduce the well-known Robotic Operating System (ROS) and learn how to deploy simple visual perception algorithms to run on general robotic platforms. The course provides the essential ingredients to enable PhD students to demonstrate AI modules on robotic platforms, such as the ARI humanoid robot who recently joined the UniTN family.

Cloud Computing & Big-Data

Institution: Scuola Superiore Sant’Anna
Location: Scuola Sant’Anna in Piazza dei Martiri della Liberta’ or closeby locations
Type: Ph.D. course
Attendance Mode: Blended
Exam: Yes
Lecturers: Tommaso Cucinotta
Email: tommaso.cucinotta@santannapisa.it
Academic Year: 2021/2022
Semester: 1
Hours: 30
Timetable: From the 2nd or 3rd week of November, please contact the lecturer for more information.
Abstract: This course provides an overview of the challenges to face, and the technical solutions to embrace, when building large-scale, fault-tolerant, distributed and replicated real-time cloud services. These systems need to be capable of serving millions/billions of requests per second with industrial-grade reliability, availability and performance, and are composed of thousands of components spanning across millions of machines, worldwide. The course focuses on design, development and operations of scalable software systems, including big-data processing and analytics, as used increasingly often for nowadays intensive computations needed to train large machine-learning and artificial intelligence models, where the huge volumes of data to handle mandates the use of heavily distributed algorithms. The course covers also basic concepts on networking architectures for data-centre and cloud computing infrastructures.

Cloud Computing & Big-Data Lab

Institution: Scuola Superiore Sant’Anna
Location: TECIP Institute of Scuola Sant’Anna in Via Moruzzi, 1
Type: Ph.D. course
Attendance Mode: Blended
Exam: Yes
Lecturers: Tommaso Cucinotta
Email: tommaso.cucinotta@santannapisa.it
Academic Year: 2021/2022
Semester: 2
Hours: 30
Timetable: From April/May 2022, contact the lecturer for more information
Abstract: This is a hands-on and applied course following up to the Cloud Computing & Big-Data course. Here, students will put in practice the theoretical/abstract concepts acquired in the general course on Cloud Computing & Big-Data. During the practical sessions, we’ll have a deep dive on such concepts as: machine virtualization and OS-level virtualization on Linux; virtual networking on Linux; programming abstractions for cloud and distributed computing; elasticity in practice; big-data programming frameworks; command-line interface for major public cloud services; popular open-source cloud platforms.

Cognitive Science for AI

Institution: Università Cattolica del Sacro Cuore
Location: Università Cattolica del Sacro Cuore, Largo Gemelli 1, 20123 Milano
Type: Ph.D. course
Attendance Mode: Blended
Exam: Yes
Lecturers: Silvia Serino e Giuseppe Riva
Email: silvia.serino@unicatt.it
Academic Year: 2021/2022
Semester: 2
Hours: 16
Timetable: 2 Marzo (9-13) 9 Marzo (9-13) 16 Marzo (9-13) 23 Marzo (9-13)
Abstract: Cognitive science is the interdisciplinary study of mind and intelligence, embracing philosophy, psychology, artificial intelligence, neuroscience, linguistics, and anthropology. In this English-taught program, you will study the mind, investigating how and why people perceive, think and act the way they do. The course will focus on human cognition – emotions, attention, decision making, etc. – and artificial intelligence and answer questions like: “Can computers interpret human emotions in a reliable way? How does our brain process all the impulses which it receives and how does it respond to virtual and augmented reality? How does communication between humans and robots work?

Compartmental Models for the Analysis of Contagion Dynamics: Inference and Global Sensitivity Analysis

Institution: Università di Firenze
Location: Viale Morgagni 59 50134 Florence, Department of Statistics, Computer Science, Applications
Type: Ph.D. course
Attendance Mode: Blended
Exam: Yes
Lecturers: Michela Baccini and Giulia Cereda
Email: michela.baccini@unifi.it
Academic Year: 2021/2022
Semester: 2
Hours: 10
Timetable: May-July 2022
Abstract: The course will provide an introduction to compartmental models for
epidemic dynamics and their use for prediction and inference.
We will illustrate frequentist calibration techniques for parameters estimation and parametric
bootstrap procedures for confidence intervals construction.
Special focus will be given to global sensitivity analysis (GSA) and Sobol’s indexes
calculation as tools to assess and characterize model uncertainties both in the phase of model
construction and in the phase of inference.
The course will include practicals on Covid19 data.
Program:
-Compartmental models: structure and assumptions
-Calibration
-Flexible modelling (spline) of time varying parameters
-Quantification of sampling variability via bootstrap
-GSA and Sobol’s Indexes
-Erlang modified SIR and SIRD
————————————————

Computational Linguistics

Institution: Università degli Studi di Napoli L’Orientale
Location: Naples, Via Duomo, 219, Palazzo di S. Maria Porta Coeli
Type: Cycle of Seminars
Attendance Mode: Blended
Exam: No
Lecturers: Johanna Monti, Maria Pia di Buono
Email: mpdibuono@unior.it
Academic Year: 2021/2022
Semester: 2
Hours: 10
Timetable: April-May
Abstract: The course will focus on the formalization of natural language so that it can be interpreted and generated by Artificial Intelligence applications.
The course will aim at investigating how to process linguistic data according to logical and mathematical models and on the development of algorithms for natural language processing (NLP).

Data Mining

Institution: Università di Pisa
Location: Polo Fibonacci, University of Pisa
Type: MSc course
Attendance Mode: Blended
Exam: Yes
Lecturers: Dino Pedreschi
Email: dino.pedreschi@unipi.it
Academic Year: 2021/2022
Semester: 1,2
Hours: 96
Timetable: https://didattica.di.unipi.it/en/master-programme-in-data-science-and-business-informatics/timetable-master-in-data-science-business-informatics/
Abstract: DATA MINING: FOUNDATIONS
The formidable advances in computing power, data acquisition, data storage and connectivity have created unprecedented amounts of data. Data mining, i.e., the science of extracting knowledge from these masses of data, has therefore been affirmed as an interdisciplinary branch of computer science. Data mining techniques have been applied to many industrial, scientific, and social problems, and are believed to have an ever deeper impact on society. The course objective is to provide an introduction to the basic concepts of data mining and the process of extracting knowledge, with insights into analytical models and the most common algorithms.
DATA MINING: ADVANCED ASPECTS AND APPLICATIONS
The second part of the course completes the knowledge of the first module with: a review of advanced techniques for the mining of new forms of data; a review of the main application areas and case studies.

Data protection and data governance for Data Science and Artificial intelligence

Institution: Scuola Superiore Sant’Anna
Location: Pisa
Type: Ph.D. course
Attendance Mode: Blended
Exam: Yes
Lecturers: Prof. G. Comandé, Prof. C. Sganga, Dr. Amram
Email: g.comande@santannapisa.it
Academic Year: 2021/2022
Semester: 2
Hours: 20
Timetable: to be determined with doctoral students
Abstract: The course offers to the candidates an introduction to the legal and ethical issues surrounding personal and non-personal data protection and governance. Specific attention is also given to the implications of legal and ethical constraints on the use of data for research, the framework to unchain in practice the FAIR principles and the role of open data in research and development.

Deep Models for Spoken Language Translation

Institution: Università di Trento
Location: Trento, via Sommarive 9, Polo Scientifico Fabio Ferrari (Povo q), Garda room
Type: Ph.D. course
Attendance Mode: In person
Exam: Yes
Lecturers: Marco Turchi, Marcello Federico
Email: iecs.school@unitn.it
Academic Year: 2021/2022
Semester: 2
Hours: 20
Timetable: From May 16, 2022 to May 27, 2022
Abstract: Recent advances in deep learning are giving the possibility to address traditional NLP tasks in a new and remarkably unified manner. One of these tasks is spoken language translation (SLT) that has the goal of translating an audio signal in a source language into a text or audio in a target language. SLT combines the challenges of three prominent tasks: automatic speech recognition (ASR), machine translation (MT), and a text to speech (TTS). We will show how so-called sequence models can be trained to solve every single task as well as a combination of them.
This course will introduce the foundations and the recent advancements behind the three building blocks of SLT: ASR, MT, and TTS, and will describe how SLT has changed thanks to the Artificial Intelligence revolution. It will address these research areas from machine learning and computational linguistic perspectives, giving emphasis to the most prominent deep learning architectures. The course will also overview recent developments in specific SLT use cases currently investigated by researchers, such as simultaneous translation, subtitling, and speech dubbing.

Dynamic Factor Model

Institution: Scuola Superiore Sant’Anna
Location: Piazza Martiri della Libertà, 33 56127 Pisa (Italia)
Type: Ph.D. course
Attendance Mode: Blended
Exam: Yes
Lecturers: Matteo Barigozzi
Email: laura.magazzini@santannapisa.it
Academic Year: 2021/2022
Semester: 2
Hours: 12
Timetable: To be decided. It should be taught in the period April-May
Abstract: Factor models are becoming increasingly popular. They are commonly used by public and private institutions (e.g., central banks and investment banks) for the analysis of large panels of time series. Recently, dynamic factor models have been proposed in the literature and can be considered as a pioneer dimension reduction technique in Big Data econometrics.

Dynamics on Complex Networks

Institution: Scuola IMT Lucca
Location: IMT School for Advanced Studies (details will be communicated)
Type: Ph.D. course
Attendance Mode: Blended
Exam: No
Lecturers: Rossana Mastrandrea, PhD
Email: rossana.mastrandrea@imtlucca.it
Academic Year: 2021/2022
Semester: 1
Hours: 10
Timetable: 3/03/2021 14:00-16:00
10/03/2021 14:00-16:00
15/03/2021 14:00-17:00
17/03/2021 14:00-17:00
Abstract: The course is highly interdisciplinary focusing both on theory and applications. It aims to carefully describe the main growth models for networks: starting from stylised facts observed in real networks different growth models with increasing complexity will be introduced. Then, the course will focus on spreading models on networks: compartmental models to study epidemics diffusion and extension to information/innovation diffusion. The possibility to generalise compartments introducing individual (or groups) behaviours will bring to Agent Based Models and application. Finally, some recent results about game theory on networks will be discussed..

Emotion detection using non-invasive biometric sensors

Institution: Università di Bari
Location: University of Bari, Dipartimento di Informatica + Microsoft Teams (to enable hybrid mode)
Type: Ph.D. course
Attendance Mode: Blended
Exam: Yes
Lecturers: Nicole Novielli
Email: nicole.novielli@uniba.it
Academic Year: 2021/2022
Semester: 2
Hours: 16
Timetable: II semestre (timetable da definire)
Abstract: Course Overview. Research on affective computing investigates emotion recognition and simulation since decades. Indeed, emotions are a fundamental component of our everyday life: they influence our cognitive skills, influence the outcome of activities requiring creativity and problem-solving skills, and contribute to the success of communication and collaborative activities.
Early recognition of negative emotions, such as stress, frustration, and anger can enable just-in-time corrective actions in many application fields, including wellbeing of knowledge workers, assistive technologies, computer-mediated communication, human-computer interaction, and so on. Thus, we envision the emergence and adoption of tools for enhancing emotion awareness during software development.
In this study, we will focus on the problem of reliable identification of the emotions using non-invasive biometrics. We will survey the state-of-the-art in biometric-based emotion recognition, with particular focus on the use of non-invasive sensors and examines to what extent they are able to detect affective expressions when used by individuals during their daily activities
A discussion is offered about the advantages and limitations of relying on self-reported, self-assessed emotions as gold standard and on the open challenges due to differences between individuals, towards the development and deployment of reliable sensor-based emotion classifiers for real use scenarios. Finally, we will discuss recent advances in applied research that leverage biometric-based emotion recognition for supporting emotion awareness in computer-supported cooperative work, with specific focus on the emotions experienced by developers engaged in collaborative software development tasks.
The course will feature both lectures and practical sessions. The latter, in particular, will show how to process the raw signal obtained by biometric sensors in order to extract features to be used for training emotion classifiers based on supervised machine learning.

Epistemic Logic Programming

Institution: Università dell’Aquila
Location: L’Aquila, Coppito, Edificio Turing, Aula A1.10
Type: Ph.D. course
Attendance Mode: Blended
Exam: Yes
Lecturers: Stefania Costantini
Email: stefania.costantini@univaq.it
Academic Year: 2021/2022
Semester: 2
Hours: 12
Timetable: Monday 21st March, 15:30
Monday 28th March, 15:30
Monday 4th April, 15:30
Monday 11th April, 15:30
Abstract: Epistemic Logic Programs (ELPs) are an extension of Answer Set Programming (ASP) with epistemic operators that allow for a form of meta-reasoning, that is, reasoning over multiple possible worlds, though modal operators K and M. ELPs offer the same advantages as ASP, namely a fully declarative, intuitive language for modelling and problem encoding, plus the extra espressivity provided by epistemic operators. The course will cover semantic approaches to ELPs, computational complexity, and possible practical applications.

Ethics and legal dimensions of AI

Institution: Scuola Superiore Sant’Anna
Location: Pisa
Type: Ph.D. course
Attendance Mode: Blended
Exam: Yes
Lecturers: Prof. Comandé, Prof. Sganga, Dr. Amram
Email: g.comande@santannapisa.it
Academic Year: 2021/2022
Semester: 2
Hours: 20
Timetable: to be determined with students
Abstract:

The course introduces the candidates to the main ethics and legal issues related to AI design, development, and deployment under the legal and ethical framework of the European Union, with a comparison with other relevant legal systems. In this general context, it will discuss pragmatically among other documents the Assessment List for Trustworthy Artificial Intelligence (ALTAI) for self-assessment along with the Horizon Europe guidelines on Ethics by Design and Ethics of Use Approaches for Artificial Intelligence

European Statistical System and Data Production Model

Institution: Università di Pisa
Location: Pisa, Polo didattico le Piagge
Type: MSc course
Attendance Mode: Blended
Exam: Yes
Lecturers: Monica Pratesi – Francesco Schirripa Spagnolo
Email: francesco.schirripa@ec.unipi.it
Academic Year: 2021/2022
Semester: 1
Hours: 42
Timetable: Monday 12:15 -13:45 , Thursday 12:15-13:45
Abstract: At the end of the course the student will have knowledge on

1) the European Statistical System (ESS), the Data Production Model (DPM) and the main economic aggregates to study poverty and on the surveys on households (poverty and living conditions; EUSilc survey, Household Budget Survey, Labour Force Survey).

2) the main survey designs and their estimation strategy for planned domains of study (Regions); R codes for the application of the main direct estimators of the indicators (EURAREA and SAMPLE project libraries)

At the end of the course student will be able to deal with the ESS and to criticize the statistical quality of the published indicators of poverty and living conditions (Laeken Indicators).

Evolutionary Game Theory

Institution: Scuola IMT Lucca
Location: IMT School for Advanced Studies Lucca
Type: Ph.D. course
Attendance Mode: Blended
Exam: No
Lecturers: Ennio Bilancini
Email: ennio.bilancini@imtlucca.it
Academic Year: 2021/2022
Semester: 1
Hours: 20
Timetable: I can provide the tentative timetable if needed.
Please contact phd@imtlucca.it for the updated timetable.
Abstract: Evolutionary methods allow to study how behaviors and traits evolve in a population of interacting agents. The object of evolution can be a biological or cultural trait or a profile of strategies in a game. The process by which it changes can depend on fitness, imitation or optimization, possibly as the outcome of a deliberative process.

Explainable Artificial Intelligence

Institution: Scuola Normale Superiore
Location: Pisa, Piazza dei Cavalieri 7, SNS
Type: Ph.D. course
Attendance Mode: Blended
Exam: Yes
Lecturers: Fosca Giannotti
Email: fosca.giannotti@sns.it
Academic Year: 2021/2022
Semester: 1
Hours: 20
Timetable: Martedì 16-18 – Aula Fermi Giovedì 16-18 – Aula Bianchi SNS 14 al 23 dicembre e dal 13 gennaio
Abstract: Black box AI systems for automated decision making, often based on machine learning over (big) data, map a user’s features into a class or a score without exposing the reasons why. This is problematic not only for the lack of transparency, but also for possible biases inherited by the algorithms from human prejudices and collection artifacts hidden in the training data, which may lead to unfair or wrong decisions. The future of AI lies in enabling people to collaborate with machines to solve complex problems. Like any efficient collaboration, this requires good communication, trust, clarity and understanding. Explainable AI addresses such challenges and for years different AI communities have studied such topic, leading to different definitions, evaluation protocols, motivations, and results. This (20 hours) course provides a reasoned introduction to the work of Explainable AI (XAI) to date, and surveys the literature with a focus on machine learning and symbolic AI related approaches. We motivate the needs of XAI in real-world and large-scale application, while presenting state-of-the-art techniques and best practices, as well as discussing the many open challenges. An XAI platform with collection of many of the recently proposed algorithms will be presented on specific use cases and it will be possible familiarize with some of the methods.
The course is organized as follows in two modules: i) an introductory one providing motivations, main concepts and the library, this module of 8 hours might be delivered during the AI PhD “intensive tutorial week” (the exact planning is under discussion by the PhD AI doctoral board); ii) an advanced one where the students will actively participate to monographs topics with readings interleaved with interventions of international scholars working on the sector. This second module will be planned in the weeks after the “intensive tutorial week”. The schedule will depend also by the availability of the international scholars.

Foundations of Probability and Statistical Inference

Institution: Scuola IMT Lucca
Location: Lucca, Piazza San Francesco, 19, classroom 1 or 2
Type: Ph.D. course
Attendance Mode: Blended
Exam: Yes
Lecturers: Irene Crimaldi
Email: irene.crimaldi@imtlucca.it
Academic Year: 2021/2022
Semester: 1
Hours: 30
Timetable: November 2, 11, 16, 25, 30, December, 2, 7, 9, 14, 20, 22, January, 10, 13, 14, 17 and exam February, 4
Abstract: Learning Outcomes:
– By the end of this course, students will:
– have the ability to employ the fundamental tools of Probability Theory in order to solve different kinds of problems,
– have the fundamental concepts of Statistical Inference in order to perform various kinds of
statistical analysis,
– appreciate the importance of mathematical formalization in solving probabilistic problems and in performing statistical analysis,
– be able to independently read mathematical and statistical literature of various types and be life-long learners who are able to independently expand their probabilistic and statistical expertise when needed.
Abstract:
This course covers the fundamental concepts of probability theory and statistical inference. Some proofs are sketched or omitted in order to have more time for examples, applications and exercises.

Game Theory

Institution: Scuola IMT Lucca
Location: IMT School for Advanced Studies Lucca
Type: Ph.D. course
Attendance Mode: Blended
Exam: No
Lecturers: Ennio Bilancini
Email: ennio.bilancini@imtlucca.it
Academic Year: 2021/2022
Semester: 1
Hours: 20
Timetable: I can give the tentative one if needed.
Please contact phd@imtlucca.it for the updated timetable.
Abstract: The course provides a detailed discussion of state of the art in the modeling of interactive decisionmaking as games. Special attention will be given to the prediction of outcomes in strategic situations. For this purpose, prominent solution concepts of games are reviewed and discussed, together with their main refinements based on rationality and information requirements.

Human-Centered AI (HCAI): an innovative vision for designing intelligent systems

Institution: Università di Bari
Location: Bari, Via Orabona 4, Department of Computer Science
Type: Ph.D. course
Attendance Mode: Blended
Exam: Yes
Lecturers: Rosa Lanzilotti
Email: rosa.lanzilotti@uniba.it
Academic Year: 2021/2022
Semester: 2
Hours: 16
Timetable: April-May
Abstract: With advances in artificial intelligence new types of tools and systems are created. Some people speak of an AI revolution, in scale similar to the industrial revolution that fundamentally changed tools, technologies, and society. So far, many AI-based applications and services are not focusing on humans and their needs and desires, nor their concerns and worries, i.e. they are not human-centered. Such systems do not adequately support end users because of their poor explainability, lack of control, lack of reliability and trust. These factors, among others, show that designers and developers have to become aware of the potential ethical and practical issues of this type of system as well as to acquire proper knowledge, methodologies and techniques to create effective intelligent systems that may better satisfy their users.
In this respect, a new perspective is emerging that aims at reconsidering the centrality of human beings while reaping the benefits of AI systems to augment rather than replace professional skills: Human-centred AI (HCAI). Its goal is to promote an innovative vision of intelligent systems that are equipped with powerful algorithms but are useful and usable for people, that support them to achieve their goals, and that balance system autonomy and control by people.
The course aims to guide the student from the basics of HCAI, exploring principles, challenges and methods to design and evaluate AI-based systems. Then the course will focus on emerging aspects of HCI for AI, for example, the Explainability for AI systems, general principles for designing Human-AI Interaction. Students will be stimulated to include some topics related to their research activities.

Inferential Statistics

Institution: Università di Pisa
Location: Department of Economics and Management University of Pisa
Type: Ph.D. course
Attendance Mode: Online
Exam: No
Lecturers: Stefano Marchetti and Francesco Schirripa Spagnolo
Email: stefano.marchetti@unipi.it
Academic Year: 2021/2022
Semester: 2
Hours: 20
Timetable: 24 January 10.00-12.00
25 January 10.00-12.00 + 14.00-16.00
26 January 10.00-12.00
27 January 10.00-12.00 + 14.00-16.00
28 January 10.00-12.00
31 January 10.00-12.00
1 February 10.00-12.00 + 14.00-16.00
Abstract: Provide background of fundamental statistical theory, basic ideas of probability, modelling and tools of general statistical thinking

Information Retrieval

Institution: Università di Pisa
Location: Officine Garibaldi, via Gioderti 39, Pisa
Type: Post graduate Master course
Attendance Mode: Blended
Exam: No
Lecturers: Paolo Ferragina
Email: paolo.ferragina@unipi.it
Academic Year: 2021/2022
Semester: 2
Hours: 42
Timetable: TBA
Abstract: The module provides the description of a search engine structure and of Text Mining tools, by analyzing their characteristics and limits with respect to the computational cost, the precision/recall/F1 parameters, and the expressivity of the supported queries. The module is also based on hands-on activities that will present well-known open-source Python tools for the crawling and analysis of web pages, the semantic annotation of texts (TagMe), and the indexing of text data collections (ElasticSearch).

Intelligent Systems for Pattern Recognition

Institution: Università di Pisa
Location: Polo Fibonacci, University of Pisa
Type: MSc course
Attendance Mode: Blended
Exam: Yes
Lecturers: Davide Bacciu
Email: davide.bacciu@unipi.it
Academic Year: 2021/2022
Semester: 2
Hours: 48
Timetable: https://didattica.di.unipi.it/en/master-programme-in-computer-science/timetable-master-computer-science/
Abstract: The course introduces students to the analysis and design of advanced machine learning models for modern pattern recognition problems and discusses how to realize advanced applications exploiting computational intelligence techniques.
The course is articulated in four parts. The first part introduces basic concepts and algorithms concerning pattern recognition, in particular as pertains sequence and image analysis. The next two parts introduce advanced models from two major learning paradigms, that are (deep) neural networks and generative models, and their use in pattern recognition applications. The last part will go into the details of the realization of selected recent applications of AI models. Presentation of the theoretical models and associated algorithms will be complemented by introductory classes on the most popular software libraries used to implement them.
The course hosts guest seminars by national and international researchers working on the field as well as by companies that are engaged in the development of advanced applications using machine learning models.
Topics covered – Bayesian learning, graphical models, deep learning models and paradigms, deep learning for machine vision and signal processing, advanced neural network models (recurrent, recursive, etc.), (deep) reinforcement learning, signal processing and time-series analysis, image processing, filters and visual feature detectors, pattern recognition applications (machine vision, bio-informatics, robotics, medical imaging, etc), introduction to machine learning and deep learning libraries.

Introduction to Agent-based Economics & Agent-Based Macroeconomics

Institution: Scuola Superiore Sant’Anna
Location: Piazza Martiri della Libertà, 33 56127 Pisa (Italia)
Type: Ph.D. course
Attendance Mode: Blended
Exam: No
Lecturers: Andrea Roventini, Marcelo Pereira, Francesco Lamperti
Email: francesco.lamperti@santannapisa.it
Academic Year: 2021/2022
Semester: 2
Hours: 35
Timetable: To be decided. The first 10 hours should be given in March-April, while the remaining 25 in April-May
Abstract: Agent-based models are increasingly employed across natural and social sciences to study the behaviour of complex evolving systems. The first module of the course (Introduction to Agent-based Economics, 10 hours) introduces and discusses the building blocks of agent-based economic models, their behavioural foundations, simulation techniques, empirical validation and use as virtual policy laboratories. The second module (Agent-Based Macroeconomics, 25 hours) provides a comprehensive overview of agent-based modelling in macroeconomics, focusing on the explanation of aggregate economic phenomena (e.g. the emergence of financial crises, boom and bust credit cycles, divergence in growth) as emergent properties stemming from the interactions of boundedly rational heterogenous economic agents.

Introduction to causal inference

Institution: Università di Firenze
Location: Firenze, Viale Morgagni 59, Dipartimento di Statistica, Informatica, Applicazioni
Type: Ph.D. course
Attendance Mode: Blended
Exam: Yes
Lecturers: Fabrizia Mealli, Alessandra Mattei
Email: fabrizia.mealli@unifi.it
Academic Year: 2021/2022
Semester: 2
Hours: 10
Timetable: june 2022
Abstract: The potential outcome approach. The assignment mechanism. Design and
analysis of randomized experiments. Design and analysis of observational studies with
regular assignment mechanisms. Causal inference in irregular designs: Causal studies with
intermediate variables, Regression discontinuity designs, Causal studies where units are
clustered or organized in networks. Miscellanea: Machine Learning and Causal inference;
Difference-in-differences; synthetic controls; causal inference in time series setting.

Introduction to Network Science

Institution: Scuola IMT Lucca
Location: IMT School for Advanced Studies Lucca, p.zza San Francesco 19, 55100 Lucca (Italy)
Type: Ph.D. course
Attendance Mode: Blended
Exam: Yes
Lecturers: Tiziano Squartini
Email: tiziano.squartini@imtlucca.it
Academic Year: 2021/2022
Semester: 1
Hours: 20
Timetable: The timetable may be sbject to change: please write to phd@imtlucca.it and ask to have the calendar shared.
Abstract: The course offers a panoramic view of network science. Following its historical development, we will review the main concepts and methods of this discipline. Moving from the basic stylized facts characterizing real-world networks we will describe the most popular techniques to extract information from them.

Introduction to SAT and SMT

Institution: Università di Trento
Location: Trento, via Sommarive 9, Polo Scientifico Fabio Ferrari (Povo q), Garda room
Type: Ph.D. course
Attendance Mode: In person
Exam: Yes
Lecturers: Roberto Sebastiani
Email: iecs.school@unitn.it
Academic Year: 2021/2022
Semester: 1
Hours: 20
Timetable: From January 17, 2022 to January 28, 2022
Abstract: Propositional Satisfiability (SAT) is the problem of deciding the satisfiability of a Boolean formula (and to a wider extent, to perform Boolean reasoning on possibly-huge expressions.) The ability of efficiently reasoning with Boolean values and operators is a core feature of many applications in various fields of Artificial Intelligence, Formal Verification and Digital Electronics, including, e.g., Automated Reasoning and Planning, Formal verification of Hardware and Software devices, Knowledge Representation and Reasoning. Satisfiability Modulo Theories (SMT) is the problem of deciding the satisfiability of a (typically quantifier-free) first-order formula with respect to some decidable first-order theory (e.g., those of linear arithmetic, Arrays, and Bit-Vectors) and of their combination. SMT is being recognized as increasingly important due to its applications in many domains in different communities. In the last decade we have witnessed a striking interest in SMT, with a boost in the performances of SMT solvers, which combine the power of SAT solvers with the expressivity of decision procedures for various theories of interest. This course aims at providing an overview of the main problems, techniques, functionalities and applications of SAT and SMT. We believe that this course will be of interest for students and researchers in many domains, ranging from Formal Verification, Digital Electronics, and various fields of Artificial Intelligence (e.g. Automated Reasoning, Planning, Knowledge Representation and Reasoning).

Introduction to Statistical Decision Theory

Institution: Università di Firenze
Location: Firenze, Viale morgagni 59, Dipartimento di Statistica, Informatica, Applicazioni
Type: Ph.D. course
Attendance Mode: Blended
Exam: Yes
Lecturers: Silvia Bacci
Email: silvia.bacci@unifi.it
Academic Year: 2021/2022
Semester: 2
Hours: 10
Timetable: April/May 2022
Abstract: Starting from the origins of the modern Statistics, the course
frames classical and Bayesian approaches to statistical inference in a decisional
perspective. Links among sample data, prior information and decisional processes are
outlined, illustrating the basic concepts that characterize the Classical statistical
decision theory and the Bayesian statistical decision theory. A special focus is devoted
to illustrating the axiomatic basis of expected utility theory and its main empirical
violations.
Program: Statistics and Decisions. Utility theory. Elicitation of the utility function.
Classical statistical decision theory and Bayesian statistical decision theory. Statistics,
causality, and decisions.

Introduction to sustanability and ecological economics

Institution: Scuola IMT Lucca
Location: IMT Schooll for Advanced Studies, Piazza S. Francesco 19, Lucca
Type: Ph.D. course
Attendance Mode: Blended
Exam: No
Lecturers: Angelo Facchini
Email: angelo.facchini@imtlucca.it
Academic Year: 2021/2022
Semester: 2
Hours: 20
Timetable: Sept. 13, 2022 – Oct. 25, 2022
Abstract: This course aims to provide students with fundamental concepts of sustainability science and the economics view of the environment.
Upon completion, participants will have the knowledge and skills to:
1. Have a basic understanding of the principles of environmental and ecological economics, with a clear overview on the fundamental principles for the understanding of human-economy- environment interaction
2. Have a basic understanding of environmental problems and environmental policies.
3. Have a first knowledge of the current research topics, directions, and funding opportunities.
Participants will also rely on the main topics regarding the European Green Deal and the Ecological transition.

Kernel Smoothing

Institution: Università di Firenze
Location: Firenze, Viale Morgagni 59, Dipartimento di Statistica, Informatica, Applicazioni
Type: Ph.D. course
Attendance Mode: Blended
Exam: Yes
Lecturers: Agnese Panzera
Email: agnese.panzera@unifi.it
Academic Year: 2021/2022
Semester: 2
Hours: 10
Timetable: May-June 2022
Abstract: Kernel smoothing refers to a general class of techniques for non-parametric
estimation of functions. The course offers an overview of the applications of kernel
smoothing idea to density estimation and regression problems, along with some related
issues.

Learning and Indexing Visual Representations

Institution: Consiglio Nazionale delle Ricerche
Location: Pisa, via G. Moruzzi, 1
Type: Ph.D. course
Attendance Mode: Online
Exam: No
Lecturers: Giuseppe Amato, Fabrizio Falchi, Claudio Gennaro
Email: fabrizio.falchi@cnr.it
Academic Year: 2021/2022
Semester: 2
Hours: 12
Timetable: To be defined
Abstract: The course aims at introducing the problems and offering instruments that allow analyzing and extracting information from visual data, to search them on a large scale.

Lectures on Computational Linguistics – AILC Associazione Italiana Linguistica Computazionale

Institution: Università degli Studi di Napoli L’Orientale
Location: Naples
Type: Summer/Winter School
Attendance Mode: Blended
Exam: No
Lecturers: TBD
Email: jmonti@unior.it
Academic Year: 2021/2022
Semester: 2
Hours: 24
Timetable: 1-3 giugno 2022 TBC
Abstract: The interdisciplinary nature of the school characterizes two main areas: the humanistic one, characterized by topics related to linguistics and investigations in the field of digital humanities, and the technological one, linked to the topics of computer science, computer engineering and, in particular, artificial intelligence and cognitive sciences.

Machine Translation

Institution: Università degli Studi di Napoli L’Orientale
Location: Naples, Via Duomo, 219, Palazzo di S. Maria Porta Coeli
Type: Cycle of Seminars
Attendance Mode: Blended
Exam: No
Lecturers: Johanna Monti, Maria Pia di Buono
Email: jmonti@unior.it
Academic Year: 2021/2022
Semester: 2
Hours: 10
Timetable: May
Abstract: The course will explain the basic principles of machine translation (MT), the task of translating from one natural language to another natural language, using different approaches. A main focus of the course will be the current state-of-the-art neural machine translation technology which uses deep learning methods to model the translation process.

Markov processes

Institution: Scuola IMT Lucca
Location: Lucca, Piazza San Francesco, classroom 1 or 2
Type: Ph.D. course
Attendance Mode: Blended
Exam: Yes
Lecturers: Irene Crimaldi
Email: irene.crimaldi@imtlucca.it
Academic Year: 2021/2022
Semester: 2
Hours: 12
Timetable: February, 8, 11, 15, 18, 22, 25
Abstract: Learning Outcomes:
– By the end of this course, students will:
– be familiar with Markov processes with discrete state space and discrete or continuous time,
– be able to employ the fundamental tools of Markov processes theory in order to solve different kinds of problems,
– appreciate the importance of mathematical formalization in solving probabilistic problems,
– be able to independently read mathematical and statistical literature on Markov processes.
Abstract:
This course covers the fundamental results regarding Markov processes with discrete state space and discrete or continuous time. Some proofs are sketched or omitted in order to have more time for examples, applications and exercises.

Mathematical Models for Quantitative Finance: Market Microstructure, Networks, and Systemic Risk

Institution: Scuola Normale Superiore
Location: Pisa, piazza dei Cavalieri 7
Type: Ph.D. course
Attendance Mode: Blended
Exam: Yes
Lecturers: Fabrizio Lillo
Email: fabrizio.lillo@sns.it
Academic Year: 2021/2022
Semester: 2
Hours: 40
Timetable: https://www.sns.it/en/corsoinsegnamento/mathematical-models-quantitative-finance-market-microstructure-networks-and
Abstract: The first goal of the course is to introduce the fundamental notions of market microstructure, network modeling, and financial systemic risk. The second goal is to present some recent contributions of the scientific literature and open problems. The third goal is to provide the student with the tool for the empirical and computational analysis of high frequency data and data relevant for systemic risk.

Mathematics for Complex Systems

Institution: Università di Bologna
Location: Bologna, Dipartimento di Matematica, Piazza Porta San Donato 5
Type: Post graduate Master course
Attendance Mode: Blended
Exam: Yes
Lecturers: Daniele Tantari
Email: a.barbieri@unibo.it
Academic Year: 2021/2022
Semester: 2
Hours: 48
Timetable: https://www.unibo.it/it/didattica/insegnamenti/insegnamento/2021/469920
Abstract: At the end of the course the student : – has in-depth knowledge of the possible applications of complex system’s theory to the study of statistical inference and machine learning problems; – is able to introduce a stochastic generative model, set up an inference procedure to extract information from data and discuss process complexity and theoretical limits of the inference/learning performance from the perspective of the theory of complex systems and phase transitions

MATLAB for Data Science

Institution: Scuola IMT Lucca
Location: Lucca, Piazza S. Francesco 18, IMT School for Advanced Studies, Classroom to be defined
Type: Ph.D. course
Attendance Mode: Blended
Exam: No
Lecturers: Giorgio Gnecco
Email: giorgio.gnecco@imtlucca.it
Academic Year: 2021/2022
Semester: 2
Hours: 20
Timetable: Lectures currently scheduled in the following days: 28/9/2022 (4pm-6pm), 5/10/2022 (11am-1pm), 12/10/2022 (11am-1pm), 17/10/2022 (2pm-4pm), 20/10/2022 (11am-1pm), 24/10/2022 (11am-1pm), 25/10/2022 (11am-1pm), 26/10/2022 (11am-1pm), 27/10/2022 (11am-1pm), 28/10/2022 (11am-1pm)
Abstract: The course provides MATLAB implementations of several machine learning techniques.

Metodi matematici per l’Intelligenza Artificiale

Institution: Istituto Nazionale di Alta Matematica “Francesco Severi”
Location: Lecce
Type: Post graduate Master course
Attendance Mode: Blended
Exam: Yes
Lecturers: Adriano Barra
Email: adriano.barra@gmail.com
Academic Year: 2021/2022
Semester: 2
Hours: 63
Timetable: We do not know it yet. (please note that the course is held at Università del Salento, my University, while I belong here for INdAM, that does not have courses on its own)
Abstract: It is a course on “statistical mechanics of neural networks” that belongs to Theoretical Artificial Intelligence.
The goal of the course is to provide the students with the mathematical tools required to describe information processing in neural networks in order to infer emerging properties (namely not immediately evident by looking at the mechanisms of action of the single nodes of the network, i.e. the neurons) of large assemblies of interacting units.
The models treated will be the Sherrington-Kirkpatrick spin-glass (the archetype of a complex system, where Parisi theory lies) and its applications to neural nets, at first the Hopfield neural network and the restricted Botzmann machine, then other more complex architectures.
The ultimate reward(s) of looking at AI from this perspective are
-given the biological resemblance that shines via this route, a possible better comprehension of machine learning (toward eXplainble AI) and,
-via the construction of phase diagrams specific for any considered model, a possible optimization of machine learning (toward Optimized AI)

Minimise algorithmic bias in Collaborative Decision Making with Design Fiction (SEMINAR)

Institution: Università di Pisa
Location: TBD
Type: Ph.D. course
Attendance Mode: In person
Exam: No
Lecturers: Alessio Malizia
Email: alessio.malizia@unipi.it
Academic Year: 2021/2022
Semester: 1, 2
Hours: 4
Timetable: TBD
Abstract: Minimise algorithmic bias in Collaborative Decision Making with Design Fiction

Multimodal Machine Learning

Institution: Università di Trento
Location: online
Type: Ph.D. course
Attendance Mode: Online
Exam: Yes
Lecturers: Wei Wang, Cigdem Beyan
Email: iecs.school@unitn.it
Academic Year: 2021/2022
Semester: 1
Hours: 20
Timetable: From February 16, 2022 to March 17, 2022
Abstract: Multimodal Machine Learning is a multi-disciplinary research field. It is based on integrating and modeling multiple modalities, e.g., acoustics and vision. This course includes fundamental concepts related to multimodal learning such as multimodal alignment, multimodal fusion, joint learning, temporal learning, multimodal representation learning. We will mainly cover the recent state-of-the-art papers, which propose effective computational algorithms for diverse application spectrum.
The first part mainly focuses on human face analysis from multimedia content, such as images and videos, together with the related machine learning methods. The tasks include both the traditional ones, (e.g., face detection & recognition, age & gender estimation) and some recent ones, (e.g., face synthesis & expression manipulation). In the meanwhile, the related deep learning approaches will also be introduced, such as the convolutional neural networks, recurrent neural networks, and some specific generative models.
The second part of this course particularly focus on human behavior understanding that includes verbal and nonverbal behavior analysis and multimodal affect recognition. In these contexts, several datasets, sensing approaches, computational methodologies allowing to detecting and understanding several social and psychological phenomena will be covered. We will also point out existing limitations and outline possible future directions.
The course evaluation will be based on a small project given to a group of students (i.e., teamwork).

Neural Networks and Deep Learning: implementation issues

Institution: Scuola Superiore Sant’Anna
Location: Grey Room – TeCIP Institute, Via Moruzzi 1, Pisa
Type: Ph.D. course
Attendance Mode: Blended
Exam: Yes
Lecturers: Giorgio Buttazzo, Alessandro Biondi
Email: giorgio.buttazzo@santannapisa.it
Academic Year: 2021/2022
Semester: 2
Hours: 30
Timetable: http://retis.sssup.it/~giorgio/courses/neural/scheduleNN-2022.pdf
Abstract: The objective of the course is to provide practical and implementation issues useful to deploy neural networks on a variety of embedded platforms using different languages and development environments. Preferably for students with a background in computer science or engineering.

Neural Networks and Deep Learning: theoretical foundations

Institution: Scuola Superiore Sant’Anna
Location: Grey Room – TeCIP Institute, Via Moruzzi 1, Pisa
Type: Ph.D. course
Attendance Mode: Blended
Exam: Yes
Lecturers: Giorgio Buttazzo
Email: Giorgio.Buttazzo@santannapisa.it
Academic Year: 2021/2022
Semester: 2
Hours: 30
Timetable: http://retis.sssup.it/~giorgio/courses/neural/scheduleNN-2022.pdf
Abstract: The objective of the course is to provide basic concepts and methodologies on the main existing neural models, explaining how to use them for pattern recognition, image classification, signal prediction, data analysis, system identification, and adaptive control.

Neuro-symbolic AI (with applications to NLP)

Institution: Universita di Modena e Reggio Emilia
Location: Reggio Emilia
Type: Ph.D. course
Attendance Mode: Online
Exam: Yes
Lecturers: Marco Lippi
Email: marco.lippi@unimore.it
Academic Year: 2021/2022
Semester: 2
Hours: 12
Timetable: http://agentgroup.unimore.it/Lippi/teaching.html
Abstract: Combining symbolic and sub-symbolic approaches to perform learning and reasoning tasks has long been a major goal for artificial intelligence. Nowadays, due to the success of deep learning, this research area is mostly focusing on the integration of domain knowledge within deep neural networks, with the twofold aim of improving performance and to increase interpretability. The course will present the application of this kind of techniques to the field of natural language processing, with specific reference to the legal domain and to argument mining.

Numerical methods for machine learning

Institution: Università di Siena
Location: DIISM, University of Siena, Via Roma 56, San Niccolò Building, Siena
Type: Ph.D. course
Attendance Mode: In person
Exam: Yes
Lecturers: Maria Lucia Sampoli
Email: marialucia.sampoli@unisi.it
Academic Year: 2021/2022
Semester: 2
Hours: 20
Timetable: September 27, 29 (2022) – October 4, 6 (2022)
Abstract: This course will cover a large set of topics that are about numerical methods for Machine Learning, ranging from basic linear algebra tool to optimization methods for neural networks.

Optimal Control

Institution: Scuola IMT Lucca
Location: Lucca, Piazza S. Francesco 18, IMT School for Advanced Studies, Classroom to be scheduled
Type: Ph.D. course
Attendance Mode: Blended
Exam: Yes
Lecturers: Giorgio StefanoGnecco
Email: giorgio.gnecco@imtlucca.it
Academic Year: 2021/2022
Semester: 1
Hours: 20
Timetable: 11/1/2022: 9-11; 12/1/2022: 11-13; 14/1/2022: 9-11; 18/1/2022: 14-16; 19/1/2022: 14-16; 21/1/2022: 11-13; 24/1/2022: 11-13; 26/1/2022: 11-13; 28/1/2022: 11-13; 31/1/2022: 11-13
Abstract: The course provides an overview of optimal control theory for the deterministic and stochastic cases. Both discrete-time and continuous-time problems are considered, together with some applications to economics.

Ph.D. Courses provided by the Ph.D. in Computer Science

Institution: Università di Pisa
Location: Dipartimento di Informatica, University of PisaDepartment of Computer Science, University of Pisa, Largo Bruno Pontecorvo 3, Pisa
Type: Ph.D. course
Lecturers: Various
Academic Year: 2021/2022
Abstract: The Computer Science Department of the University of Pisa provides Ph.D. courses on different topics.
More details are available at the provided link.

Philosophy and Neuroscience in Moral Reasoning

Institution: Scuola IMT Lucca
Location: IMT Lucca
Type: Ph.D. course
Attendance Mode: Blended
Exam: Yes
Lecturers: Gustavo Cevolani and Camilla Colombo
Email: gustavo.cevolani@imtlucca.it
Academic Year: 2021/2022
Semester: 2
Hours: 14
Timetable: TBD
Abstract: The analysis of moral reasoning and surrounding topics { how to assess \good” and \bad” actions, how to choose between different moral principles, how to justify these choices {is a classical problem of moral philosophy. More recently, moral psychologists started tackling those problems using a descriptive, empirically based approach. Even more recently, neuroethicists” began investigating the neural correlates of moral judgment and the implications of neuroscientific results for moral philosophy. In the meantime, behavioral economists started addressing issues like fairness, altruism, reciprocity and social preferences, documenting the influence of (broadly construed) moral considerations on human decision-making. The course is an introduction to the analysis of moral reasoning at the interface between neuroscience, moral psychology, moral philosophy, and economics. We shall explore problems concerning the biological and neural bases of moral thinking, the role of emotions in moral reasoning, the economic way of interpreting moral behavior, the significance of empirical results for normative theories of morality, and some methodological issues arising within neuroethics.

Philosophy of Science

Institution: Scuola IMT Lucca
Location: IMT Lucca
Type: Ph.D. course
Attendance Mode: Blended
Exam: No
Lecturers: Gustavo Cevolani
Email: gustavo.cevolani@imtlucca.it
Academic Year: 2021/2022
Semester: 1
Hours: 20
Timetable: November- December 2021
Abstract: The course provides an introduction to the basic concepts and problems in the philosophical analysis of scientific reasoning and inquiry. We will focus on some central patterns of reasoning and argumentation in science and critically discuss their features and limitations. Topics covered include the nature of theory and evidence, the logic of theory testing, and the debate about the aims of science and the trustworthiness of scientific results. We shall discuss classical examples and case studies from the history and practice of science to illustrate the relevant problems and theoretical positions. Students will freely engage in brainstorming on these topics and are welcome to propose examples, problems, and methods from their own disciplines..

Programming and Data Analytics (PDA)

Institution: Scuola Superiore Sant’Anna
Location: Piazza Martiri della Libertà, 33 56127 Pisa (Italia)
Type: MSc course
Attendance Mode: Blended
Exam: Yes
Lecturers: Andrea Vandin, Daniele Licari
Email: andrea.vandin@santannapisa.it
Academic Year: 2021/2022
Semester: 2
Hours: 40
Timetable: https://github.com/EMbeDS-education/StatsAndComputing20212022/wiki
Abstract: This course will provide a well-structured introduction to the fundamental principles of (object-oriented) programming with applications to data processing and machine learning through a 2-Module structure. A student who has met the objectives of the course will acquire an understanding of the issues and tasks involved in structured computer programming, data analysis and machine learning, as to be able to make informed decisions. The student will be able to write Python programs of various nature, with a focus on complex data analysis tasks.
It is possible to attend single modules.

Module 1 introduces students to the fundamental principles of structured programming, with basic applications to data processing. It starts from basic notions of programming (variables, data types, collections, control & repetition structures, functions & modules), and progresses to basic data processing functionalities (loading, manipulation, and visualization of CSV data).

Module 2 introduces students to the components of typical data analysis processes and machine learning pipelines. It first builds the necessary toolset by introducing popular Python libraries for data manipulation/visualization (NumPy, Pandas, Seaborn, scikit-learn) with simple applications. The toolset is then applied to a more complex case study on the classification of benign and malignant breast cancer, including aspects of data preprocessing, dimensionality reduction, clustering, and classification. The module also presents KNIME, a popular python-integrated workflow-based language for data analysis.
Prerequisites: Knowledge of computer programming obtained attending Module 1.

Prerequisites: No prerequisites.
Evaluation Group project with final presentation and Jupyter notebook documentation.
Materials: Learning Python (M. Lutz). Statistics and Machine Learning in Python (E.Duchesnay, T.Löfstedt, F.Younes).

Programming for data science

Institution: Università di Pisa
Location: Polo Fibonacci, University of Pisa
Type: MSc course
Attendance Mode: Blended
Exam: Yes
Lecturers: Franco Maria Nardini
Email: francomaria.nardini@isti.cnr.it
Academic Year: 2021/2022
Semester: 1
Hours: 96
Timetable: https://didattica.di.unipi.it/en/master-programme-in-data-science-and-business-informatics/timetable-master-in-data-science-business-informatics/
Abstract: This is an introductory course to computer programming and related mathematical/logic background for students without a Bachelor in Computer Science or in Computer Engineering. The objective is to smoothly introduce the student to the programming concepts and tools needed for typical data processing and data analysis tasks. The course consists of lectures and practice in computer labs. The student will be able to use computer programming languages and related mathematical notions for problem reasoning and solving. The student will be able to separate apart the problem constraint and solutions from the actual coding in a specific computer programming language. Computational thinking is the expected ability at the end of the course.

Quantitative Finance

Institution: Scuola Normale Superiore
Location: Pisa, Piazza dei Cavalieri 7
Type: Ph.D. course
Attendance Mode: Blended
Exam: Yes
Lecturers: Fabrizio Lillo, Giulia Livieri, Giacomo Bormetti (seminars)
Email: classi@sns.it
Academic Year: 2021/2022
Semester: 1
Hours: 40
Timetable: https://www.sns.it/it/corsoinsegnamento/quantitative-finance
Abstract: The student will have the familiarity with the elements of the stochastic calculus and with the main models describing the random evolution of the financialprices. He/She will be able to compute the price of derivative options and to discuss the assumptions of the different modelling choices. He/She will be able to estimate volatility, implied volatility and risk premia.

R and Stata for Data Science

Institution: Scuola IMT Lucca
Location: IMT Lucca
Type: Ph.D. course
Attendance Mode: Blended
Exam: No
Lecturers: Francesco Serti
Email: francesco.serti@imtlucca.it
Academic Year: 2021/2022
Semester: 2
Hours: 20
Timetable: 13,14,17,30 June + 6,7,8 July
Abstract: This course aims to provide students with Stata and R language fundamentals to conduct data management and exploratory analysis and implement various econometric and machine learning techniques to address typical research questions in economics.

Random effects models for multilevel and longitudinal data

Institution: Università di Firenze
Location: Firenze, Viale Morgagni 59, Dipartimento di Statistica, Informatica, Applicazioni
Type: Ph.D. course
Attendance Mode: Blended
Exam: Yes
Lecturers: Leonardo Grilli and Carla Rampichini
Email: leonardo.grilli@unifi.it
Academic Year: 2021/2022
Semester: 2
Hours: 15
Timetable: January/February 2022
Abstract: The course introduces the theory and practice of random effects (mixed effects)
models for the analysis of multilevel data in both cross-sectional and longitudinal settings.
Emphasis is placed on model specification and interpretation. The course covers random
effects models for continuous responses and for categorical responses.

Sicurezza dei sistemi informatici

Institution: Università di Sassari
Location: University of Sassari
Type: MSc course
Attendance Mode: Blended
Exam: Yes
Lecturers: Massimo Tistarelli
Email: tista@uniss.it
Academic Year: 2021/2022
Semester: 2
Hours: 60
Timetable: Not available yet
Abstract: This course deals with security issues in modern networked computer systems, paying special attention to data security and protection of computer networks and networked computer applications, in a closed (Intranet) or open (Internet) environment.
The course aims to teach the skills needed to perform both the analysis and the high-level design of the security features of IT components and systems.

Social Network Analysis

Institution: Consiglio Nazionale delle Ricerche
Location: Pisa
Type: Post graduate Master course
Attendance Mode: In person
Exam: Yes
Lecturers: Andrea Passarella
Email: chiara.boldrini@iit.cnr.it
Academic Year: 2021/2022
Semester: 2
Hours: 20
Timetable: https://masterbigdata.it/it/didattica
Abstract: This course introduces students to the theories, concepts and measures of Social Network Analysis (SNA), that is aimed at characterizing the structure of large-scale Online Social Networks (OSNs). The course presents both classroom teaching to introduce theoretical concepts, and hands-on computer work to apply the theory on real large-scale datasets obtained from OSNs like Facebook and Twitter. The course aims to discuss in particular how the structural properties of social networks can be analyzed through SNA techniques, and how these properties can be used to characterize social phenomena arising in the society.

Social network analysis

Institution: Università di Pisa
Location: Polo Fibonacci, University of Pisa
Type: MSc course
Attendance Mode: Blended
Exam: Yes
Lecturers: Dino Pedreschi
Email: dino.pedreschi@unipi.it
Academic Year: 2021/2022
Semester: 2
Hours: 48
Timetable: https://didattica.di.unipi.it/en/master-programme-in-data-science-and-business-informatics/timetable-master-in-data-science-business-informatics/
Abstract: Over the past decade there has been a growing public fascination with the complex “connectedness” of modern society. This connectedness is found in many contexts: in the rapid growth of the Internet and the Web, in the ease with which global communication now takes place, and in the ability of news and information as well as epidemics and financial crises to spread around the world with surprising speed and intensity. These are phenomena that involve networks and the aggregate behavior of groups of people; they are based on the links that connect us and the ways in which each of our decisions can have subtle consequences for the outcomes of everyone else. This course is an introduction to the analysis of complex networks, with a special focus on social networks and the Web – their structure and function, and how it can be exploited to search for information. Drawing on ideas from computing and information science, applied mathematics, economics and sociology, the course describes the emerging field of study that is growing at the interface of all these areas, addressing fundamental questions about how the social, economic, and technological worlds are connected. Data-driven analysis of complex networks using a variety of models and software tools.

Socio-Economic Networks

Institution: Scuola IMT Lucca
Location: IMT School for Advanced Studies Lucca, P.zza San Francesco 19, 55100 Lucca (Italy)
Type: Ph.D. course
Attendance Mode: Blended
Exam: Yes
Lecturers: Massimo Riccaboni
Email: massimo.riccaboni@imtlucca.it
Academic Year: 2021/2022
Semester: 1
Hours: 20
Timetable: The timetable may be subject to change: please write to phd@imtlucca.it and ask to have the calendar shared.
Abstract: The topic of the course will be the analysis of socio-economic networks. The course will consist of three parts: (1) basic notions of social network analysis; (2) micro-level networks of individuals and firms; (3) meso and macro-level networks of sectors and countries. The first part will focus on some basic notions of social network analysis. Individual and inter organizational networks will be analyzed in the second part, with a special focus on peer effects and the division of labor within and across firm boundaries. The third part on the empirics of meso and macro networks in economics will focus on international trade, human mobility, production, and finance. All parts will give you a brief overview of the literature, which predominantly adopted an econometric approach to network analysis.

Statistical and Machine Learning Models for Time Series Analysis

Institution: Scuola Normale Superiore
Location: Pisa, piazza dei Cavalieri 7
Type: Ph.D. course
Attendance Mode: Blended
Exam: Yes
Lecturers: Fabrizio Lillo, Giulia Livieri
Email: giulia.livieri@sns.it
Academic Year: 2021/2022
Semester: 2
Hours: 40
Timetable: https://www.sns.it/en/corsoinsegnamento/statistical-and-machine-learning-models-time-series-analysis
Abstract: The objective of the course is to provide the main elements of the theory of time series analysis by using methods from statistics, econometrics, and machine learning. The course also provides working knowledge for the computational modeling of empirical time series as well as for the simulation and inference of statistical models.

Statistical Learning and Large Data (SLLD)

Institution: Scuola Superiore Sant’Anna
Location: Piazza Martiri della Libertà, 33 56127 Pisa (Italia)
Type: MSc course
Attendance Mode: Blended
Exam: Yes
Lecturers: Francesca Chiaromonte
Email: francesca.chiaromonte@santannapisa.it
Academic Year: 2021/2022
Semester: 2
Hours: 40
Timetable: https://github.com/EMbeDS-education/StatsAndComputing20212022/wiki
Abstract: This course will introduce the students to various topics in contemporary Statistical Learning and to methods used to analyze the large, complex datasets that are increasingly common in many scientific fields. The content will be organized in two Modules. Compared to traditional courses, the focus will be on analyzing actual datasets of interest to the students through group projects and Practicum sessions associated to each lecture. It is possible to attend single modules.

Module 1
– Unsupervised classification; Clustering methods
– Unsupervised dimension reduction; Principal Components Analysis and related techniques
– Supervised classification methods
– Non-parametric regression methods
– Resampling methods, Cross Validation, the Bootstrap and permutation-based techniques.

Module 2
– Feature selection and regularization techniques for high-dimensional Linear and Generalized Linear Models
– Feature screening algorithms for ultra-high dimensional supervised problems
– Supervised dimension reduction; Sufficient Dimension Reduction and related techniques
– Subsampling/partitioning approaches for ultra-high sample sizes
– Under- and oversampling approaches for data rebalancing

Evaluation: Group project with final presentation and written report.

Materials: An Introduction to Statistical Learning – with Applications in R (James, Witten, Hastie, Tibshirani; Springer). Computer Age Statistical Inference (B. Efron, T. Hastie). Links to further material will be provided as needed.

Prerequisites: a working knowledge of basic statistical inference (point estimation, confidence intervals, testing) and linear and generalized linear models. E.g., this may be obtained, or refreshed, through course Applied Statistics.

Statistical Mechanics

Institution: Università di Bologna
Location: Bologna, Dipartimento di Matematica,Via di Porta San Donato 5
Type: Post graduate Master course
Attendance Mode: Blended
Exam: Yes
Lecturers: Pierluigi Contucci
Email: a.barbieri@unibo.it
Academic Year: 2021/2022
Semester: 1
Hours: 48
Timetable: https://www.unibo.it/en/teaching/course-unit-catalogue/course-unit/2021/469867/orariolezioni
Abstract: At the end of the course the student is acquainted with the basic notions of statistical mechanics like, for instance, the concept of equilibrium probability measure. He is able to deal with the study of new scientific results, theoretical and applied, where statistical mechanics is involved.

Statistical methods for data science

Institution: Università di Pisa
Location: Polo Fibonacci, University of Pisa
Type: MSc course
Attendance Mode: Blended
Exam: Yes
Lecturers: Salvatore Ruggieri
Email: salvatore.ruggieri@unipi.it
Academic Year: 2021/2022
Semester: 2
Hours: 48
Timetable: https://didattica.di.unipi.it/en/master-programme-in-data-science-and-business-informatics/timetable-master-in-data-science-business-informatics/
Abstract: The student who completes successfully the course will have a solid knowledge on the main concepts and tools of statistical analysis, including the definition of a statistical model, the inference of its parameters with confidence intervals, the use of hypothesis testing. and some basic knowledge of the statistics of linear time series. Finally the student will be able to use the language R for performing statistical analyses. The student will be able to understand the main concept of statistical analysis and to choose and apply the appropriate tool to the case under study. The student will also be able to use the language R for performing statistical analyses.

Stochastic Processes and Stochastic Calculus

Institution: Scuola IMT Lucca
Location: Lucca, Piazza San Francesco, 19, classroom 1 or 2
Type: Ph.D. course
Attendance Mode: Blended
Exam: Yes
Lecturers: Irene Crimaldi
Email: irene.crimaldi@imtlucca.it
Academic Year: 2021/2022
Semester: 2
Hours: 20
Timetable: April, 21, 26, 28 and May, 2, 5, , 9, 12, 16, 19, 23 and exam June 17
Abstract: Learning Outcomes:
By the end of this course, students will:
– be familiar with some important stochastic processes,
– be familiar with Ito stochastic calculus,
– be able to identify appropriate stochastic model(s) for a given research problem,
– appreciate the importance of mathematical formalization in solving probabilistic and statistical problems,
– be able to independently read mathematical and statistical literature on stochastic processes, stochastic calculus and their applications.
Abstract:
This course aims at introducing some important stochastic processes and Ito stochastic calculus. Some proofs are sketched or omitted in order to have more time for examples, applications and exercises.

Survey Methods: Traditional and New Techniques in Official Statistics

Institution: Università di Pisa
Location: Pisa, Polo didattico Le Piagge
Type: MSc course
Attendance Mode: Blended
Exam: Yes
Lecturers: Monica Pratesi – Linda Porciani- Luca Faustini- Roberta Varriale
Email: porciani@istat.it
Academic Year: 2021/2022
Semester: 2
Hours: 42
Timetable: https://esami.unipi.it/programma.php?c=51614&aa=2021&cid=120&did=17
Abstract: At the end of the course the student will have knowledge on

1) data collection methods

2) the use of secondary sources in data production

3) the use of R

At the end of the course student will be able to deal with traditional and modern data collection methods and will be able to present a simple policy paper using data from official surveys or other sources.

Symbolic and Evolutionary Artificial Intelligence

Institution: Università di Pisa
Location: Pisa, Largo Lucio Lazzarino, 1. Room still to be defined
Type: MSc course
Attendance Mode: Blended
Exam: Yes
Lecturers: Marco Cococcioni
Email: marco.cococcioni@unipi.it
Academic Year: 2021/2022
Semester: 2
Hours: 60
Timetable: https://unimap.unipi.it/registri/dettregistriNEW.php?re=3311947::::&ri=010764
Abstract: This is a 60h course for students enrolled in the Master degree in Artificial Intelligence and Data Engineering course at the School of Engineering of the University of Pisa. It is scheduled at the second year/second semester, so it assumes that the students already have attended basic courses in machine learning and artificial intelligence.
The main focus of the course is in multi-objective evolutionary optimization and reinforcement learning, but it also touches hardware accelerators for AI. In particular, we will show how to implement hardware accelerators for AI in electronics (mainly referring to the RISC-V architecture) and recent advances on hardware implementation of photonic neural networks.

Text Analytics

Institution: Università di Pisa
Location: Polo Fibonacci, University of Pisa
Type: MSc course
Attendance Mode: Blended
Exam: Yes
Lecturers: Andrea Esuli
Email: andrea.esuli@isti.cnr.it
Academic Year: 2021/2022
Semester: 1
Hours: 48
Timetable: http://didawiki.cli.di.unipi.it/doku.php/mds/txa/start
Abstract: The course targets text analytics systems and applications to respond to business problems by discovering and presenting knowledge that is otherwise locked in textual form. The objective is to learn to recognize situations in which text analytics techniques can solve information processing needs, to identify the analytic task/process that best models the business problem, to select the most appropriate resources methods and tools, to collect text data and apply such methods to them. Several applications context will be presented: information extraction, sentiment analysis (what is the nature of commentary on an issue), spam and fake posts detection, quantification problems, summarization, etc.

Text Annotation and Analysis

Institution: Università degli Studi di Napoli L’Orientale
Location: Naples, Via Duomo, 219, Palazzo di S. Maria Porta Coeli
Type: Cycle of Seminars
Attendance Mode: Blended
Exam: No
Lecturers: Maria Pia di Buono, Johanna Monti
Email: mpdibuono@unior.it
Academic Year: 2021/2022
Semester: 2
Hours: 20
Timetable: July 2022
Abstract: This course will cover the major techniques for text analysis with an emphasis on both statistical and rule-based approaches that can be generally applied to unstructured text data in any natural language with no or minimum human effort.

The Engaged Scholarship: A guide on formulating a research question (SEMINAR)

Institution: Università di Pisa
Location: TBD
Type: Ph.D. course
Attendance Mode: Blended
Exam: No
Lecturers: Alessio Malizia
Email: alessio.malizia@unipi.it
Academic Year: 2021/2022
Semester: 1, 2
Hours: 2
Timetable: N/A
Abstract: The Engaged Scholarship: A guide on formulating a research question

Theories of rationality

Institution: Scuola IMT Lucca
Location: IMT Lucca
Type: Ph.D. course
Attendance Mode: Blended
Exam: No
Lecturers: Gustavo Cevolani and Camilla Colombo
Email: gustavo.cevolani@imtlucca.it
Academic Year: 2021/2022
Semester: 2
Hours: 10
Timetable: TBD
Abstract: see link above

Time Series Analysis

Institution: Scuola Superiore Sant’Anna
Location: Piazza Martiri della Libertà, 33 56127 Pisa (Italia)
Type: Ph.D. course
Attendance Mode: Blended
Exam: Yes
Lecturers: To Be Decided
Email: laura.magazzini@santannapisa.it
Academic Year: 2021/2022
Semester: 2
Hours: 20
Timetable: To be decided. It should be in the period February-March
Abstract: [TENTATIVE] The course provides a working knowledge of the modern methods used in macroeconomics and, to some extent, in finance. Students will be exposed to the methodologies employed for the analysis of time series econometrics, ranging from classic tools such as linear stationary processes (ARMA, VAR) to techniques that have recently entered the macroeconomist toolbox (Bayesian and high dimensional estimation).

Time Series and Mobility Data Analysis

Institution: Università di Pisa
Location: Officine Garibaldi, via Gioderti 39, Pisa
Type: Post graduate Master course
Attendance Mode: Blended
Exam: No
Lecturers: Mirco Nanni, Riccardo Guidotti
Email: mirco.nanni@isti.cnr.it
Academic Year: 2021/2022
Semester: 2
Hours: 36
Timetable: TBA
Abstract: The purpose of the course is to introduce the main techniques in data mining and machine learning (including deep learning approaches) for the analysis of temporal data, in particular for time series and spatio-temporal data related to human mobility. The presentation will be supported by several case studies developed with the SoBigData.eu Laboratory.

Visual analytics

Institution: Università di Pisa
Location: Polo Fibonacci, University of Pisa
Type: MSc course
Attendance Mode: Blended
Exam: Yes
Lecturers: Salvatore Rinzivillo
Email: salvatore.rinzivillo@isti.cnr.it
Academic Year: 2021/2022
Semester: 2
Hours: 48
Timetable: https://didattica.di.unipi.it/en/master-programme-in-data-science-and-business-informatics/timetable-master-in-data-science-business-informatics/
Abstract: The module aims at preparing students to the approprieted presentation of data and knowledge extracted from them through visualization tools and narratives that exploit multimedia.
The module first presents the basic visualization techniques for the effective presentation of information from several different sources: structured data (relational, hierarchies, trees), relational data (social networks), temporal data, spatial data and spatio-temporal data.

Visual computing

Institution: Università di Sassari
Location: University of Sassari
Type: MSc course
Attendance Mode: Blended
Exam: Yes
Lecturers: Marinella Cadoni; Massimo Tistarelli
Email: maricadoni@uniss.it
Academic Year: 2021/2022
Semester: 2
Hours: 60
Timetable: Not yet available
Abstract: In Visual Computing verranno fornite le tecniche di base di elaborazione di immagini e di analisi di immagini. Partendo dalla formazione delle immagini, gli studenti apprenderanno gli algortimi fondamentali per la compressione, l’eliminazione di disturbi, l’esaltazione di particolari, l’estrazione di informazione. Sperimenteranno gli algortimi appresi nello svolgimento di mini-progetti di elaborazione di immagini provenienti da diversi sistemi di acquisizione, come fotocamere digitali e microscopi.