Course content

Data science and Sociology: methods and applications.

This module will introduce the main analytical and methodological dimensions of sociological research and will place data science approaches and methods of analysis in relation to them. At the end of the module, students will be able to:

• read and understand sociological research by identifying the research questions, the characteristics, the potential and the limits of the methodological choices made
• develop a critical awareness of the nature and uses of Big Data within sociological research
• formulate relevant research questions and design research plans that are consistent with them and that exploit the potential of Data Science in a conscious and appropriate manner
• present and communicate the results to a wider audience of potential stakeholders.

Data science and Psychology: Methods and applications

This course provides a first introduction to the most relevant constructs and theoretical models in the psychological sciences by means of a descriptive framework which highlights important connections between psychological theories and some of the most recent advances in Data Science. The main idea consists in showing how some traditional psychology research fields such as, for example, work and organizational psychology, applied cognitive psychology, and can benefit from an approach based on the use of big data. In this course, the students will learn how to recognize and appreciate the specific characteristics and features of the psychological datum as well as to manage the data analysis problem via a hierarchical and multiphasic process to extract useful and relevant information from specific empirical hypotheses.

This is a graduate level course that studies mathematical models, computational paradigms, algorithms and methodologies that look for patterns and regularities in large amounts of raw data, in order to understand natural phenomena, business operations and human behaviors, and make predictions, forecastings and performance improvements. The goal of the course is to introduce the students to the basic concepts, principles and techniques of Data Mining, to help them develop the required skills for using the state of the art data mining algorithms for solving practical problems, and to provide them with the required experience that will later allow them to operate independently, efficiently and effectively, in highly competitive markets. The course aims also to support students wishing to pursue a research carrier by teaching them the methodologies in performing independent and effective studies and research activities.At the end of the course, the students will be finding themselves familiar with the most popular data mining concepts and will be able to identify and use the right solutions for any data analysis problem they may find themselves into. Last but not least, future data scientists will also learn how to perform the right experiments, how to interpret the results correctly and how to present them in the most effective way. At the end of the course, the student will be able to manage Data Mining definitions and difference from Machine Learning & AI, Similarity Techniques, Clustering, Association Rules, Frequent Itemsets, Clustering, Recommendation Systems, Online Advertising, Classification, Dimensionality Reduction, Graph Processing, and Visualization.

The students will learn how to leverage Big Data frameworks, configure them, know what is needed in order to use them, and be clear on the benefits to expect from them. The knowledge acquired is done in two fields. The first is the processing (by introducing new programming and data processing approaches), and the second is the storage and querying (by presenting new systems designed for such data). At the end of the course, the students will be able to face real world challenges by having the ability to identify the right solutions in real life situations involving Big Data, make the right choices in putting in place, configuring, and using big data systems, and perform the required maintenance and optimization tasks. The course is fundamental for the modern data scientists since it provides them with required knowledge on the tools that are available for achieving their goals. In particular will have knowledge on:

1. Introduction to Big Data, Relational Model Principles
2. Big Data Management: Map Reduce, Unix, Virtual Machines, HDFS, Hadoop, HIVE, PIG, Spark (SQL, DataFrame, MLIB, GraphX)
3. NoSQL: MongoDB, CouchDB (Document Databases), Neo4J (Graph Databases), Oracle NoSQL / Berkeley DB, Riak (Key-Value Stores), HBase (Family Column stores), PostgresQL (Relational extensions).

The objective of the English Test at B2 level is to measure a candidate's linguistic competences in listening, reading, writing and speaking according to the B2 competence descriptors in the CEFR (The Common European Framework of Reference for Languages). The tests require candidates to demonstrate their competence in English in a range of academic, personal and professional contexts. The result of the test is PASS or FAIL. The result conveys whether or not a candidate would be able to cope with a reasonable degree of autonomy in an English-medium academic / familiar professional context.

Module Statistical Methods: the student will learn the principles and practice of statistical inference, with a focus on the likelihood-based approach and the linear regression model. In particular, after a brief review of the basic principles of probability and random variables, the first part of the course will allow students:

• to develop a deep understanding of the concept of likelihood function and its characteristics
• to perform maximum likelihood estimation
• to perform hypothesis testing and construct confidence intervals through the likelihood ratio method and its variants.

The second part of the course will develop the knowledge of the linear regression modeling framework and the ability to apply it in different practical contexts. Therefore, at the end of the course, the student should be able:

• to specify and estimate a linear regression model according to the empirical situation under study
• to perform hypothesis testing to compare models and construct confidence intervals for model parameters and for predictions
• to detect and deal with the main violations of model assumptions: multicollinearity, heteroscedasticity and correlated errors.

Module Statistical Models: after successful completion of the module students are able to understand and apply the basic notions, concepts, and methods of computational linear algebra, convex optimization and statistical multivariate methods for data analysis and dimension reduction problems. They master generalized linear models for the analysis of discrete variables, etc. and the use of the singular value decomposition, principal components analysis and random matrices for low dimensional data representations. They know techniques such as Linear and Quadratic Discriminant Analysis, Multidimensional Scaling, Factor and Correspondence Analysis. They know basics of sparse recovery problems, including compressed sensing, low rank matrix recovery, and dictionary learning algorithms.

The course aims to introduce students to the study of the different legal issues related to data management. Basics for understanding the legal aspects will therefore be provided initially. Particular attention will be paid to the phenomena that go under the names of "Open Data" and "Big Data", followed by the study of intellectual property rights (copyright, sui generis right on databases, etc.) and the contractual instruments that allow their circulation (licences). Finally, the focus will be on data protection rules, with particular attention to the management of research data.

The course intends to bring the students a solid general understanding of data, information and knowledge management focusing on concepts, methods and tools that can be used to enable innovation and change management within the organizations. A particular focus will be proposed on service science, an interdisciplinary approach to the study, design, and implementation of services systems. Organizations are complex systems in which specific interdependencies of people and technologies take actions and provide value. The course enable the student to deeply study theories, methods and techniques of Information and Knowledge management, Information system and data management, open data, double web platforms, innovative business models based on open innovation and some emerging technological phenomena such as crowdsourcing and gamification. At the end of the course, students will be able to identify the processes of creating and managing information and business knowledge. They will be able to understand how to manage information and knowledge processes that influence change management. Finally, pupils will be able to use gamification mechanics to promote effective management of information, knowledge and the behaviors of actors involved.

The course aims at providing a basic introduction to the concepts and the tools for data exploration and visualization, through class lectures and lab sessions. The core of the class is the exploration of the theoretical foundations and the practice of the diverse dimensionality reduction strategies, from the basic procedures to the more advanced state-of-the-art algorithms. Further, basics of clustering theory will also be shown. These topics will be complemented by a discussion on the principles of data visualization through the different types of graphics. At the end of the course, students will be able to: ● describe the overall structure of a multidimensional dataset; ● effectively project a multidimensional dataset in a lower dimensional space highlighting the main features; ● choose a suitable graphical representation to pinpoint one or more quantitative aspects of the dataset; ● write the code to implement the chosen graph into one of the languages/environments shown during class.

The course aims to give a broad introduction to Machine Learning, in particular under an applicative perspective. The classes are evenly divided in theoretical and practical, where in the first part the student will be driven through a few possible machine learning approaches meant to face different applicative tasks, the second part instead will require a more "hands on" approach, from the data acquisition, to the training process management and the usage of tools of machine learning. During the course, the students will learn to analyze the approach to a Machine Learning project in its entireness. In the end, the students will be able to assess the type of algorithm to use (supervised, unsupervised, few-shot, etc.), interpreting results and learning to work for objectives. The lab classes will be done in Python, using open-source toolboxes. The topics of the final project will be on general machine learning.

Programming module: the goal of the course is to introduce the Python programming language, one of the most widely used scientific computing languages, and related technologies. At the end of this course, the students will be able to: a. remember the syntax and semantics of the Python language; b. understand programs written by others individuals; c. analyze a simple data analysis task and reformulate it as a programming problem; d. evaluate which features of the language (and related scientific libraries) can be used to solve the task; e. construct a Python program that appropriately solves the task; f. evaluate the results of the program.

Algorithms and Data Structure module: the overall goal of this course is to introduce students to the design and analysis of algorithmic solutions, through the presentation of the most important class of algorithms and the evaluation of their performance. At the end of the course, students will be able to: a. describe classic algorithms and understand their behavior; b. understand, at the basic level, the most important algorithm design techniques; c. evaluate algorithmic choices and select the ones that best suit their problems; d. analyze the complexity of algorithms; e. design simple algorithmic solutions to basic problems, and to implement them using the Python language.

The course aims at providing with a basic working knowledge of linear algebra, and of elementary calculus. After successfully attending the course, the students will be able to:

• understand the basic concept of linear algebra and elementary calculus
• compute with agility with vectors and matrices
• compute with agility with the derivatives of simple functions
• understand the concept of eigenvalues and eigenvectors
• compute eigenvalues and eigenvectors in simple examples.

The students will learn how to leverage Big Data frameworks, configure them, know what is needed in order to use them, and be clear on the benefits to expect from them. The knowledge acquired is done in two fields. The first is the processing (by introducing new programming and data processing approaches), and the second is the storage and querying (by presenting new systems designed for such data). At the end of the course, the students will be able to face real world challenges by having the ability to identify the right solutions in real life situations involving Big Data, make the right choices in putting in place, configuring, and using big data systems, and perform the required maintenance and optimization tasks. The course is fundamental for the modern data scientists since it provides them with required knowledge on the tools that are available for achieving their goals. In particular will have knowledge on: 1) Introduction to Big Data, Relational Model Principles, 2) Big Data Management: Map Reduce, Unix, Virtual Machines, HDFS, Hadoop, HIVE, PIG, Spark (SQL, DataFrame, MLIB, GraphX) 3) NoSQL: MongoDB, CouchDB (Document Databases), Neo4J (Graph Databases), Oracle NoSQL / Berkeley DB, Riak (Key-Value Stores), HBase (Family Column stores), PostgresQL (Relational extensions)

The objective of the English Test at B2 level is to measure a candidate's linguistic competences in listening, reading, writing and speaking according to the B2 competence descriptors in the CEFR (The Common European Framework of Reference for Languages). The tests require candidates to demonstrate their competence in English in a range of academic, personal and professional contexts. The result of the test is PASS or FAIL. The result conveys whether or not a candidate would be able to cope with a reasonable degree of autonomy in an English-medium academic / familiar professional context.

Module: “Statistical Methods” - The student will learn the principles and practice of statistical inference, with a focus on the likelihood-based approach and the linear regression model. In particular, after a brief review of the basic principles of probability and random variables, the first part of the course will allow students ● to develop a deep understanding of the concept of likelihood function and its characteristics; ● to perform maximum likelihood estimation; ● to perform hypothesis testing and construct confidence intervals through the likelihood ratio method and its variants. The second part of the course will develop the knowledge of the linear regression modeling framework and the ability to apply it in different practical contexts. Therefore, at the end of the course, the student should be able: ● to specify and estimate a linear regression model according to the empirical situation under study; ● to perform hypothesis testing to compare models and construct confidence intervals for model parameters and for predictions; ● to detect and deal with the main violations of model assumptions: multicollinearity, heteroscedasticity and correlated errors.

Module “Statistical Models” - After successful completion of the module students are able to understand and apply the basic notions, concepts, and methods of computational linear algebra, convex optimization and statistical multivariate methods for data analysis and dimension reduction problems. They master generalized linear models for the analysis of discrete variables, etc. and the use of the singular value decomposition, principal components analysis and random matrices for low dimensional data representations. They know techniques such as Linear and Quadratic Discriminant Analysis, Multidimensional Scaling, Factor and Correspondence Analysis. They know basics of sparse recovery problems, including compressed sensing, low rank matrix recovery, and dictionary learning algorithms.

The module aims at providing an understanding of the main computational research methods that are specific to online media data and to analyse social processes with an emphasis to 'big data' sources. The module presents an overview of current cutting-edge methodology in quantitative methods related to online social research: Web surveys, online experiments, opinion mining techniques, social network analysis, computational statistical models At the end of the module, students will be able to: ● understanding the main principles at the core of the different computational methods that are applied to social science datasets; ● a firm grasp of the coding process using software for automatic text analysis; ● apply social network analysis to the context of combining network information with other type of data; ● understanding what is opinion mining and what are its core principles and techniques; ● use computational models to analyse large survey data through techniques such as model based recursive partitioning, latent class analysis, relational class analysis.

The course aims to introduce students to the study of the different legal issues related to data management. Basics for understanding the legal aspects will therefore be provided initially. Particular attention will be paid to the phenomena that go under the names of "Open Data" and "Big Data", followed by the study of intellectual property rights (copyright, sui generis right on databases, etc.) and the contractual instruments that allow their circulation (licences). Finally, the focus will be on data protection rules, with particular attention to the management of research data.

The course aims at providing a basic introduction to the concepts and the tools for data exploration and visualization, through class lectures and lab sessions. The core of the class is the exploration of the theoretical foundations and the practice of the diverse dimensionality reduction strategies, from the basic procedures to the more advanced state-of-the-art algorithms. Further, basics of clustering theory will also be shown. These topics will be complemented by a discussion on the principles of data visualization through the different types of graphics. At the end of the course, students will be able to: ● describe the overall structure of a multidimensional dataset; ● effectively project a multidimensional dataset in a lower dimensional space highlighting the main features; ● choose a suitable graphical representation to pinpoint one or more quantitative aspects of the dataset; ● write the code to implement the chosen graph into one of the languages/environments shown during class.

The course aims to give a broad introduction to Machine Learning, in particular under an applicative perspective. The classes are evenly divided in theoretical and practical, where in the first part the student will be driven through a few possible machine learning approaches meant to face different applicative tasks, the second part instead will require a more "hands on" approach, from the data acquisition, to the training process management and the usage of tools of machine learning. During the course, the students will learn to analyze the approach to a Machine Learning project in its entireness. In the end, the students will be able to assess the type of algorithm to use (supervised, unsupervised, few-shot, etc.), interpreting results and learning to work for objectives. The lab classes will be done in Python, using open-source toolboxes. The topics of the final project will be on general machine learning.

Students must include further 12 CFU of courses of their choice. A study plan with courses of their choice listed in the tables below is automatically approved. In all other cases, the study plan must be completed and submitted to the commission for a control of congruence with the learning objectives of the master in Data Science.

The course’s objective is an assessment of the most relevant issues on the interface of intellectual property rights (IPRs) and competition law under the perspective of agents operating in dynamic markets at a national, European and international level. Knowledges: intellectual property law, Antitrust legislation, theories of competition, innovation economics. Competencies: planning efficient juridical strategies concerning the management and development of the intellectual capital of a firm, assessing problems and opportunities deriving from the application of Antitrust legislation to dynamic markets.

The course covers some basic/intermediate psychometric models for conducting empirical quantitative research in the Human-computer interaction (Hci) field. The statistical psychometric procedures will be illustrated using the R statistical package. Topics in the course will include models for experimental design, questionnaires and surveys (both paper and pencil format and online format), social network analysis, and mouse-tracking data. At the end of the course the students will be able to manage the different measurement scales involved in the measurement of cognitive processes in Hci tasks and to select the most adequate data analysis techniques for such type of data. Moreover, the student will also become familiar with the main psychometric models to analyze latent psychological dimensions as well as observed psychological variables by using the statistical package R.

The goal of the laboratory is to provide knowledge and practical skills on managing large amounts of data to support decision-making processes within companies. In particular, the type of data that the Data Scientist will have to use within corporate organizations will be emphasized. The analyses will be covered covering all departments of a company across the board, transforming the data into information that can be understood by the company management. At the end of the course the students will be able to identify the data to be used within the company and analyse them in order to support strategic decisions.

This course discusses theories and methods for the analysis of social network data, and illustrates how these methods can help answer specific types of research questions in the social sciences. More specifically, students:

• will learn how social network analysis can help answer research questions in areas, such as sociology, anthropology, psychology, epidemiology, criminology, political science, communication science, management, and educational research
• will become familiar with (statistical) methods to explore such social network research questions
• will learn how to apply their knowledge to specific datasets.

The objective of the laboratory is to provide students with practical sessions to analyze data coming from socio-economic datasets using computational methods. Examples of such methods are the use of machine learning techniques for classification, models for pattern identification, relational class analysis, and others. All aimed at the analysis of multi-dimensional social phenomena, at the needs related to market research, governance and public administration, ex post analysis of the impact of policies. At the end of the course the students will be able to collect, organize and use data from public opinion research companies, public and private organizations and analyse them using data science computational techniques.

The course furnishes students with the necessary competences to tackle issues at the centre of the debate in contemporary social science applying quantitative techniques to appropriate data. The course starts from the connection of theory and empirical research inherent in the scientific research process. It treats topics like the translation of a sociologically relevant question into empirical research, the selection of data, and the use of appropriate methods and techniques to test hypothesis.

The course looks into social inequalities from a variety of points of view: measurement issues, drivers and consequences of inequalities, policies to reduce inequalities and tools to evaluate the impact of those policies. At the end of the course, students will be able:

• to make conscious use of key inequality measures;
• to place the ongoing debate on inequality in an appropriate way in the global context;
• to set up the design for the evaluation of the effects of public policies for reducing inequality and combating poverty.

The course aims at presenting the institutionalist approach to economic and labor sociology, making use of tools coming from comparative political economy and labour market economy&sociology. The course will be built as a seminar course, with a preliminary part mainly 'front lesson' that will introduce the macro basis of the course (macro theories and perspectives from economic and labor sociology and comparative political economy). Following a macro-micro-macro approach, basic sociological tool for the course is the "Coleman’s Boat" schema proposed by James Coleman (Foundations of Social Theory, 1990) applied to the “internal analysis of system behavior”.

In the second part of the course/seminar, students will have to discuss journal articles and prepare (and present) their own 'Coleman’s Boat' applied to their specific topic of interest. Other students will act as discussants for each presentation. A great deal of activation is requested to students. The course aims at providing students with a mastery in understanding (and subsequently proposing) a link between macro dimensions (theories, regimes, perspectives, etc) and micro-level actors’ behaviours and choices, that ri-aggregate in societal outcomes of interests for social sciences (social risks f.e.).

At the end of the course, students are expected to have understood the meaning of doing "Sociology as Social Science" and of performing "Theory-based empirical research". The presentation will substitute the final exam of the course.

The course aims to enable students to acquire both the main notions of planning, analysis, design and implementation of a project, and the ability to use some software tools that can facilitate project management activities. In particular, the course aims to enable students to know and apply some fundamental techniques for project management, such as work breakdown structures (WBS), the critical path method (CPM), and Gantt charts.

The laboratory aims to provide the necessary basis for learning how to manage, analyse and visualize geospatial data through open source tools (geospatial libraries for python, qgis, R ...) At the end of the course, students will be able to:

• apply spatial statistics methods and models
• understand the specificity of the geospatial data model
• elaborate and integrate geospatial data (vector and raster)
• create maps (also accessible via the web).

The course aims at providing students with the quantitative skillset needed to model consumer and buyer preferences and predict marketplace behaviour to help the making of informed business and management decisions. Students are expected to learn how to identify, acquire, manage and analyze data – through the use of the open-source statistical environment R – to address real-case decision-making problems. In particular, at the end of the course student should be able to: perform traditional and choice-based conjoint analysis to reveal the hidden drivers of consumer choice; predict consumer choice; identify customer targets to support the implementation of target marketing activities; perform data-based (post hoc) market segmentation; predict customer retention and churn phenomena; perform data-based product positioning and market basket analysis; analyze consumer sentiment.

The laboratory aims of the Data Journalism at providing, through a multidisciplinary approach, the development and application of the set of processes necessary to manipulate data (acquisition, cleaning, analysis and visualization of data) having as a result an effective communication composed of text and graphics. The core of the lab will be the development of the entire process from the exploration and research of different data sources and their processing in order to be able to identify an effective visualization and / or infographic according to the type of data and based on storytelling. During the course, important contributions will be made by experts in the various fields covered by the course. We will also introduce the techniques of mashup from different remote services, programming in Python and manipulation of data in various formats, specific tools for data cleaning, scraping, data analysis and graphic and cartographic representation.

At the end of the course, students will be able to:

• Develop a story and define the most appropriate storytelling
• Develop data acquisition pipelines according to different sources
• Take care of the cleanliness of the data and the appropriate statistical manipulation
• Describe the overall structure of a multidimensional data set based on temporal and spatial dimensions
• Choose the most effective way of representing a multidimensional data set based on the content you want to communicate
• Implement the necessary code (in one of the languages/environments shown in the course) to the desired graphical representation
• Writing an article and/or report supported by the data.

The interdisciplinary course of Digital Social Data, aims to offer an introduction to the various approaches that computer scientist, economists, lawyers and sociologists adopt to approach a highly complex issue as Big data and Open data. The course will have a methodological and applied character and will introduce the student to the use of the big data. In this part, will be given to the student notions of programming and data analysis in R@ in highly complex contexts. At the end of this second part, the student:

1. will know how to identify the different methodological approaches in handling Big Data
2. will be able to evaluate the main types of analysis that can be conducted on the Big Data
3. apply the main programming tools in R @ to the Big Data; and
4. will independently conduct elemental analysis of highly complex data in R@.

Learn to apply correctly quantitative methods in social science research. Description of social phenomena and hypothesis test. we will learn to use the statistical program Stata and analyse data in a meaningful way Topics covered reach from data management and recoding of variables to multivariate analysis. Students will need to prepare own analysis putting into practice the techniques treated during the lessons.

The lab aims to put into practice the notions of data science acquired by students during the first year. The participant will have to carry out the entire data processing cycle starting from real problems, starting from the collection of data (methods and techniques) to the cleaning and validation and analysis, to the final production of statistical and visual materials and their presentation. List of problems will be provided by the teacher in contact with academic and business realities.

The aim of this course is to study the behaviour of people. The course is data intensive and hands on. It covers all the phases from experiment design, data collection, data preparation and data analysis. After a brief theoretical introduction, the course will consist of running real world experiments, on large amounts of data. The exam will consist of presenting the results of the experiment in a public presentation. This inter-disciplinary course bridges competences in sociology, ethics and computer science.

The student will learn about systems view of biological networks, where quantitative and dynamic considerations of biological properties are in the foreground. The biological entities will be studied with an emphasis on the emergent behavior resulting from the interactions of the network components. The student will thus be equipped with an interdisciplinary skill-set for studying such systems in a way that brings together notions from biology, applied mathematics and computer science. At the end of the course the students will be able to:

• Work on biological questions using analytical methods with the aim of providing systems level "holistic" answers, whereby formal methods are used to compartmentalize different aspects
• Become familiar with a spectrum of techniques from applied mathematics and computer science with respect to their strengths in various biological settings
• Use programming technologies to formalize various biological problems for simulation and choose appropriate computational tools for analyzing the simulations
• Have a working knowledge of the state-of-the-art formal methods used in studying transcription and signal transduction networks
• Work with some of the more recent, cutting-edge computational techniques used in synthetic biology such as DNA computing.

The student will be able to address statistical queries on biological datasets, and will be equipped with basic programming skills for performing statistical analysis on biological data by using programming environments such as R. At the end of the course the students should be able to:

• Recognize different kinds of datasets
• Choose the most appropriate plot for a given dataset, and identify the informal properties of the data from the plot
• Recognize different probability distributions and apply their properties on the data for inferring statistical descriptions
• Interpret the data using summary statistics as well as other semi-formal methods
• Formally infer statistical values and investigate relationships between data components using statistical tests, for example, to validate the hypothesis of an experiment
• With the aid of programming environment R, use statistics as a tool to make more informed decisions.

This is an introductory course to the increasingly important area of computer and network security. The main goal is to enable students to:

• understand the theoretical and practical problems of information security
• understand and recognize threats to fundamental security properties: confidentiality, integrity, and availability
• understand how the main security mechanisms - such as authentication and authorization protocols, and access control - can be applied to mitigate vulnerabilities.

The aim of the course is to provide knowledge of both theoretical and practical aspects of machine learning, and present the main techniques of machine learning and probabilistic reasoning. At the end of the course, students will be able to:

• describe the main machine learning techniques, with their characteristics and limitations
• master probabilistic reasoning techniques
• model simple probabilistic scenarios with Bayesian Networks
• realize learning programs tailored to the specific problem to be addressed.

The goal of this course is to provide definitions and techniques on web architectures. At the end of the course the student will be familiar with the main issues related to web architectures and with several web technologies. It is necessary to be familiar with object oriented programming and Java. A basic knowledge of computer networks (TCP/IP stack, sockets) and of Databases and SQL language is required.

The aim is to identify the important research issues, and to ascertain potentially fruitful future research directions in relation to the multimodal emotion analysis and to human-computer interaction. In particular, the course will introduce key concepts, discuss technical approaches, and open issues in the following areas: interaction of emotion with cognition and perception; the role of emotion in human-computer interaction; the communication of human emotion via face, voice, physiology, and behavior; construction of computers that have skills of emotional intelligence; the development of computers that"have" emotion; and other areas of current research interest. At the end of the course the students will know the state of the art in affective computing and will be able to write a research proposal on this theme.

The course introduces the basics of computational linguistics by giving an overview of the field. It then focuses on the syntax and semantics of natural language familiarizing students with lexicalized formal grammars and computational semantics models. The second part of the course introduces students to multimodal models by considering in particular language and vision modalities. Students will hence gain a good overview of the field, its methods and main long term goals. At the end of the course students will be able to:

1. illustrate the main challenges addressed in the field, which are its consolidated results and which are the current research questions
2. master, at introductory level, the basic rules of some formal grammars and of formal and distributional semantics languages and their integration based on the principle of compositionality
3. compare approaches on computational linguistics tasks, in particular within interactive question answering and language and vision integration
4. apply interdisciplinary approaches to linguistics tasks and write a scientific report on their research in LaTex.

The general goal of the Enterprise Information System course is to teach students to play an active role in decision making about investments in information technologies. The course introduces the fundamental principles of information systems; teaches concepts and models to plan investments in information systems according to the activities to be supported. Focusing on web-based information systems, the second part of the course introduces a method to design an effective web presence strategy. After completing the course, the student will be able to:

• know and classify information systems and their application in business
• know the principles and the protocols of Internet and Web
• apply requirements elicitation and analysis techniques
• evaluate website quality according to a goal-oriented approach - model, evaluate and plan web presence strategies - analyse and manage online reputation of a company.

This class presents a survey of methods from the fields of statistics and machine-learning aimed at extracting generalizations from example data, and use them to automatically analyze new data. The class focuses on case studies in the analysis of different components of natural language. At the end of the course, students will:

1. demonstrate knowledge of the principles of core Machine Learning techniques
2. be able to read and understand CL literature using the introduced techniques, and critically assess their use in research and applications
3. have some fundamental computational skills allowing them to run existing Machine Learning software and interpret their output.

The course presents some basic concept about mind/brain interaction and the biases that affect human performance. Such biases will be analyzed with reference to how people use and interact with technologies. At the end of the course the students will be able to understand some basic neurophysiological mechanisms involved in relevant cerebral processes associated with visual perception and motor behavior. Moreover, they will be able to understand and distinguish the closed-loop experimental paradigms in the field of Brain-computer interfaces and Human-computer interaction.

The goal of this course is to enable students to analyze various types of high-dimensional data types commonly encountered in the practice of Molecular Biology using a range of methodologies, including established statistical approaches, machine learning, and recently introduced methods. At the end of the course, students are expected to possess the following skills:

• familiarity with different types of Molecular Biology data and their specificity
• good understanding of different conceptual methods for analyzing the data
• familiarity with practical computational tools for carrying out the analysis
• ability to frame the results of their analysis in a biological context using networks
• ability to provide a functional interpretation of the results of their analysis
• ability to complete a project, write a structured report, and orally present the results of their work.

The student will develop a research project under the supervision of a teacher on a theme suggested by the teacher. At the end of the lab, students will have acquired the skills needed to develop their own project for their thesis.

Aim of the course is to give students the possibility to study, in depth, a topic covered in a general way in one of the other courses of the study program. In this course, the student has the opportunity to explore and investigate the state of the art, the theoretical foundation, the most important methods and techniques, the applications of the chosen topic. This includes performing a bibliographic search and identifying related material in textbooks under the supervision of the professor. When suitable, the student may also apply some of the covered methods and techniques. The topic will be selected in agreement with one of the professors teaching in the study program. At the end of the course, the student will have an extended knowledge of the chosen topic and learned the methodology of studying in depth a topic relevant for the study program.

The course provides an overview of the main platforms and technologies for the development of embedded systems for industrial Internet of Things (IoT) products and services—including devices for sensing, actuation, processing, and communication. The course will improve the design skills and experiences to employ in developing novel systems. The course consists of both theoretical lectures and hands-on practical activities. The lab sessions include programming exercises of IoT systems equipped with WiFi-microcontrollers and multicore programmable platforms, for fast prototyping of sensing, actuation and communication applications. The course just requires basics of computer science and covers the following topics:

• Definition of IoT, architectures, and challenges
• Programmable platforms for IoT fast prototyping (microprocessors and microcontrollers)
• Design flow and development tool-chains
• Fundamentals of Real-time Internet of Things
• Use of existing cloud services for IoT back-end.

Embedded computers are found everywhere from home appliances to automobiles to medical devices. Designing an embedded computing system is a challenging task because the requirements include manufacturing cost, performance, power consumption, user interface, hard deadlines and rich functionality. The objective is to illustrate the embedded system design process which includes requirements, specification, architecture, components and system integration phases. The course will be backed up by real-life design examples to illustrate the design process and also students will be asked to design embedded systems to gain experience.

The goal of this course is to study two of the main paradigms of Bio-Inspired Artificial Intelligence, namely: Evolutionary Computation, inspired by evolutionary biology, and Swarm Intelligence, inspired by collective behaviors of social animals. First, the main theories and algorithms will be introduced. Then, it will be shown how these techniques can be applied, for instance, for solving complex optimization problems, train data-driven models, generate new contents (video-games, websites, art), find bugs in software, evolve programs, or find innovative solutions in robotics, logistics, and engineering. Finally, it will be shown how these techniques can help the understanding of biological systems, to close the loop between biology and AI. At the end of this course, students will be familiar with the most important Evolutionary Computation and Swarm Intelligence techniques, and will be able to apply them to different contexts in industry, research, or even entertainment. They will also know the fundamentals for developing new algorithms and adapt them to new problems.

This hands-on lab will take the application of Data Science techniques to neuroimaging, linguistic and behavioural data sets. Students will be guided through accessing neuroimaging data from open source databases (e.g. openNeuro, connectomeDB), and converting data in to a format suitable for specific kinds of analyses. Throughout this course, students will develop their skills through the implementation of their own data science experiments on real data sets. At the end of this course, students will be able to address specific questions and hypotheses to draw informed conclusions about the brain and behavior using neuroimaging, linguistic and behavioural data.

Students will learn how to use deep learning techniques in realizing machine learning systems for complex data. The most popular architectures, including multi-layer perceptron, convolutional networks and recurrent neural networks will be presented. At the end of the course, students will be able to:

• choose the most appropriate architecture for the problem at hand
• choose the most appropriate optimization methods to successfully train the network
• evaluate the generalization performance of the learned network.

The course is intended to provide the fundamentals of High Performance Computing software for High-end Data Science. The course will include both theoretical and practical aspects related to simulation-centric and data-centric paradigms; it will present convergent aspects of HPC software ecosystems and data frameworks at a large-scale in relevant scientific disciplines.

The goal of this course is to provide motivations, definitions and techniques in support of the usefulness of logic in the effective and efficient modelling of data and knowledge. The course will have succeeded if it stimulates the interested students to continue their career with higher interest into logic-based models for data and knowledge representation in their own field of expertise, and to produce computer-processable solutions of relevant problems. At the end of course, the students will be able:

• to use main logics for modelling data and knowledge
• to develop elementary logic models for data and knowledge.

Goal of the course is to provide the student with the notions of data mining that are necessary in order to mine genomic and transcriptomic data and the practical skills to pre-process and mine biological data. At the end of the course the student should be able to recall and discuss the techniques presented, reading the literature about similar techniques, pre-process and mine a specific instance of biological data, report and present the results.

The instructional goal of the course is to acquire a comprehensive vision of the distributed measurements and automation systems nowadays employed in service and industrial applications. In particular, the course covers the most common technological solutions adopted in the industrial domain and also a presentation of the theoretical issues that originate when the control and/or measurement are applied following a distributed paradigm. A special emphasis will be devoted to the design of distributed estimators in industrial and robotics context, analyzing the problems involved from both the practical and the theoretical perspectives. Examples on academic and actual plants will be presented throughout the course. After completing the course, the student will be able to:

• Understand the complexity, the issues and the potentialities of a distributed system
• Compare the various different technological solutions available for a distributed system
• Evaluate and analyse the theoretical and practical implications of a distributed measurement system
• Analyse and design the distributed solution for a particular system
• Critically asses the benefits and the limitations of a distributed solution.

The course offers an introduction to big data science from the point of view of tensor decomposition. The course will begin with concrete examples of big data problems. The central part of the course will be based on geometric structures for modeling the extraction of information from problems of large data collections. Part of the course will be devoted to computational aspects.

At the end of the class the student is expected to have acquired the following skills:

• Knowledge and understanding skills.
• Good knowledge of the basic arguments of tensor decomposition from the geometric point of view and concrete examples of big data
• Ability to apply knowledge and understanding
• Inductive and deductive reasoning ability to deal with issues that are provided individually or in a group from time to time
• Autonomy of judgment
• Ability to develop logical arguments and produce correct demonstrations
• Ability to identify the most appropriate methods for analyzing, interpreting, and modeling information extraction issues from large data collections
• Communicative Skills
• Ability to expose subjects both at the written / computational level by carrying out exercises handed out by the instructor both at the oral level in the possible presentation of a topic taught at a lecture through a public seminar.

Combining concepts from topology and algorithms, the course delivers an introduction to the field of computational topology. Starting with motivating problems in both mathematics and computer science and building up from classic topics in algebraic topology and homological algebra, it addresses the theory of persistent homology. At the end of the course the student will be able to:

• master advanced techniques in algebraic topology and homological algebra, such as homology, cohomology and sheaf theory
• compute homology and cohomology groups of simplicial complexes and topological spaces
• apply nontrivial tools from algebra and geometry to data analysis.

Simple models in different areas of biology (ecology, infectious diseases, enzymatic reactions, molecular and physiological networks) will be introduced and studied in the course of Mathematical Biology. Attention will be paid both to the construction of the models, and to the analysis of the resulting mathematical problems, mainly in the area of ordinary differential equations, but introducing also partial differential equations, difference equations, and stochastic models. The analysis will be directed towards problems of biological interest, such as the species coexistence, the existence of periodic solutions, the conditions for epidemic spread, or impulse transmission. The course will be in common with the course " Mathematical Modeling" from the Master's in Quantitative and Computation Biology. The final part of the course will be devoted to the development, in a group consisting of students from both degree courses, of a project on a subject presented during the course.

At the end of the course, it is expected that students will be able to develop models for biological systems similar to those presented in the course, to perform standard qualitative analysis for systems of ordinary differential equations, will know the basic properties of reaction-diffusion equations, will be able to perform simulations in Matlab of ordinary differential equations and of reaction-diffusion systems. Moreover, thanks to the project to e developed, it is expected that all students will have improved in their ability to collaborate in an interdisciplinary setting, and to communicate the results that have been obtained.

The purpose of the course is to address the study of the foundations of Bayesian statistics starting from the basic principles of the probability calculus. The course is divided into two modules: the first one will be devoted to estimation and to hypothesis testing based on the Bayesian approach to inference and intends to highlight the similarities and differences with the classical Fisher’s approach. Particular attention will be paid to the study of some common concepts to the two inferential approaches (for example, sufficiency, likelihood, independence/exchangeability) by stressing the different interpretations and their main consequences in terms of inferential results. The students during the course will have the opportunity to familiarize themselves with the principal theorems, logical developments, ideas and issues that underlie the different statistical techniques covered and learn to recognize them as natural extensions and consequences of the concepts introduced previously in the courses of probability and mathematical statistics. Subsequently we focus on the computational aspects in Bayesian inference problems.

In the introduction part we discuss the main techniques for the generation of pseudo-random numbers from univariate and multivariate distributions. After, we learn about Monte Carlo integration and optimization. Finally Markov Chain Monte Carlo procedures are introduced, in particular the Metropolis-Hastings algorithms and the Gibbs samplers. These methods will be applied mainly to hierarchical statistical models.

The students will be request to have a pro-active participation in both classroom and in the Lab activities and a constant involvement in the training program.

The student, at the end of this class, will be able to use linear and generalized linear models to study the relation between continuous or discrete variables and predictors. She/He will learn fundamental aspects of the theory and the way to apply them to practical case through the use of a statistical software.

To give a basic knowledge on the problems concerning the statistical analysis of data from time series; especially, to develop good competences in the structure of linear ARMA models, often used in the analysis of economic data, and of the relative methods of analysis. After successful completion of the course the students are able to evaluate and study the theoretical properties of models for stationary linear stochastic processes and to use the most appropriate techniques for the specification of models and the estimation of the unknown parameters. She/He will be able to analyze real data from time series.

The course provides some basic elements of the theory of stochastic processes: Gaussian processes, stationary processes, Markov processes and martingales. At the end of the course, it is expected that students will be able to model complex phenomena using results from the theory of stochastic processes.

This course will cover basic neural anatomy and methodology for the application of the main neuroimaging techniques used in cognitive neuroscience, such as functional and structural Magnetic Resonance Imaging, Transcranial Magnetic Stimulation, Magnetoenchelalography and EEG. At the end of the course students should be able:

• To explain the following questions about brain imaging methods:
• To define what signal sources are being measured by the different methods
• To define what is the main equipment used and why
• To explain what are the relative advantages and limitations of the methods
• To describe what are the common data analysis steps used and why
• To describe and interpret example of applications in neuroscience research.

This course will cover the foundations of neuroimaging techniques. Students will obtain a basic understanding (i.e., methodological foundation) of non-invasive brain imaging techniques. The programme contains specialised modules on the theory and methods of functional and structural magnetic resonance imaging; electro- and magneto-encephalography; as well as multimodal approaches. At the end of the course, students will be able to describe the basic principles, advantages and limitations of the neuroimaging methods discussed to an extent which permits the effective application of Data Science approaches to this medium.

Behavioral economics is a relatively new branch of economics that aims to improve the descriptive and the predictive power of the economic analysis by integrating agents’ limited cognitive abilities and limited willpower that, together with informational incompleteness, cause agents to behave sub-optimally. This course offers an overview of the theories and the empirical results in the Behavioral Economics field allowing students to orientate themselves towards future deeper elaborations into this field of research.

More specifically, the course is aimed to provide students with the opportunity to:

1. acquire a general overview of several major topics and empirical evidence in Behavioral Economics
2. learn the basic tools for designing and carrying out a Behavioral Economics experiment
3. acquire the basic skills for applying the Behavioral Economics insights to real world applications.

The aim of this course is to get students acquainted with (statistics for) opinion research. We will use comparative studies, such as the European Values Study (1981-2008), European Social Survey (2002-2014) and the Survey of Health, Aging and Retirement (from 2004). The emphasis will be on applying research methods in a hands-on training that teaches students how to answer interesting questions empirically.

At the end of this course, students should be able to:

• formulate (micro-macro) research questions and hypotheses
• prepare data to test the hypothesis
• interpret the results of the tabular and (multilevel) regression analyses
• apply tabular and (multilevel) regression techniques
• write a report on an empirical multilevel analysis.

The course aims to introduce various methods for decision and risk analysis. For the decision support systems part, special emphasis will be placed on multi-criteria methods and game theory as a tool for analysing strategic behaviour. For the risk analysis part, various sources of risk, their representation and the tools that can be used for risk mitigation will be discussed.

The following are skills that the student should acquire with the attendance of the course.

• A knowledge of a set of mathematical models and algorithms suitable for some optimization problems in industrial and management framework.
• The ability to classify different optimization problems and to recognize which types of algorithm and software are the most suitable in order to face a submitted problem
• The capability to understand and manage the results of numerical implementation of the algorithms.

Functional Magnetic Neuroimaging (fMRI) is a popular technique to study the human brain. Since its advent, the technique and its analyses have kept moving forward thus helping to better capture the complex organization of the brain and its functions. This course will introduce the students to advanced fMRI analyses such as multivariate pattern analysis, representational similarity analysis, pattern classification algorithms, and resting-state functional connectivity, with a particular focus on providing the students with extended hands-on experience on these forefront methods. By the end of this course, students will be able to apply cutting-edge analytic techniques to neuroimaging data.

The main objective of this SECOND SEMESTER course is to teach basic image analyses steps commonly used in functional neuroimaging studies with magnetic resonance imaging (MRI) and magnetoencephalography (MEG). 2-hour classes will take place at Rovereto three times per week in a computer lab where students will use neuroimaging analyses tools and data. Lectures will cover basic introductory topics but will be mostly focused on a tutorial style showing how to perform basic analyses, both with guided-class and take-home exercises. Students will choose one of the two methods (MRI or MEG), and at the end of the course they should be able to perform the basic analyses

The purpose of the course is to provide students with practical, other than theoretical, knowledge of various research tools in the economics discipline so that they can plan and conduct good research. The research tools covered in this course are the “questionnaire” and the "quantitative analysis of economic data". Regarding the questionnaire related tools, the student will learn to: build a questionnaire, build a coding plan and enter the data in electronic format, build simple scales measuring attitudes and personality and run simple statistics. Regarding the tools to manage data -also longitudinal-, the student will learn to build a longitudinal database and to apply frontier quantitative methods to analyze it. The course is intended as a laboratory where students will be asked to demonstrate the acquisition of the applied skills through a personal Lab activity.

The course will introduce to business measures and methods aimed at: the description of the competitive environment and the internal state of an organization; the prediction of future trend; the evaluation of the impact of decisions. Along the course, frontal lectures will alternate with presentations and discussion of assignments proposed by the instructor. Students will make a direct experience of selecting and finding sources of data, and use them to build a system of performance analysis.

At the end of the course, students are expected to be able to:

• Identify knowledge needs and define precisely knowledge questions
• Identify data, metrics and methods to answer to knowledge questions
• Be aware of methods and instruments for the collection, elaboration and analysis of data
• Understand what data and methods can be used for description, prediction and business decisions.

The attending student will be engaged in a team work, that will help develop her/his ability to work in teams, discuss critically and improve communication skills