Corso di laurea: Data science for management Programmazione per l'A.A. 2021/2022
Lo studente espliciterà le proprie scelte al momento della presentazione, tramite il sistema informativo di ateneo, del piano di completamento o del piano di studio individuale, secondo quanto stabilito dal regolamento didattico del corso di studio.
Data for sciences
Primo anno
Primo semestre
| Insegnamento | CFU | SSD | Ore Lezione | Ore Eserc. | Ore Lab | Ore Altro | Ore Studio | Attività | Lingua | ||||||||||||||||||||||||||||
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9793926 -
DATA BASE AND BIG DATA ANALYTICS
(obiettivi)
The course covers the fundamental concepts of management and design of database systems.
Topics include data models (relational); query languages (SQL); implementation techniques of database management systems (index structures and query processing); and noSQL databases. The learning objectives are: a) To understand and use the main technologies for database management; b) To design a relational database (and not), from a conceptual, logical and physical perspective; c) To use SQL language for performing efficient queries in cases of large datasets; and d) To create and query large scale datasets. |
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DATA BASE
(obiettivi)
The course covers the fundamental concepts of management and design of database systems.
Topics include data models (relational); query languages (SQL); implementation techniques of database management systems (index structures and query processing); and noSQL databases. The learning objectives are: a) To understand and use the main technologies for database management; b) To design a relational database (and not), from a conceptual, logical and physical perspective; c) To use SQL language for performing efficient queries in cases of large datasets; and d) To create and query large scale datasets. |
6 | ING-INF/05 | 40 | - | - | - | - | Attività formative caratterizzanti | ENG | ||||||||||||||||||||||||||||
| - BIG DATA ANALYTICS | Erogato anche in altro semestre o anno | ||||||||||||||||||||||||||||||||||||
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9793875 -
DATA ANALYSIS AND STATISTICAL LEARNING
(obiettivi)
1. Knowledge and understanding (Conoscenza e capacità di comprensione). The first “Statistical Learning” module mainly concerns the fundamentals of two of the main methods used in unsupervised learning: principal component analysis and cluster analysis.
2. Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, the student will be able: i) to implement the main methods used in unsupervised learning; ii) to summarize the main features of a dataset and extract knowledge from data properly. 3. Making judgements (Autonomia di giudizio). On completion, the student will be able to choose a suitable statistical model, apply it, and perform the analysis using a statistical software. 4. Communication skills (Abilità comunicative). On completion, the student will be able to present the results from the statistical analysis, and which conclusions can be drawn. 5. Learning skills (Capacità di apprendimento). On completion, the student will be able to understand the structure of unsupervised learning. |
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DATA ANALYSIS
(obiettivi)
DATA ANALYSIS
1. Knowledge and understanding (Conoscenza e capacità di comprensione). The first “Statistical Learning” module mainly concerns the fundamentals of two of the main methods used in unsupervised learning: principal component analysis and cluster analysis. 2. Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, the student will be able: i) to implement the main methods used in unsupervised learning; ii) to summarize the main features of a dataset and extract knowledge from data properly. 3. Making judgements (Autonomia di giudizio). On completion, the student will be able to choose a suitable statistical model, apply it, and perform the analysis using a statistical software. 4. Communication skills (Abilità comunicative). On completion, the student will be able to present the results from the statistical analysis, and which conclusions can be drawn. 5. Learning skills (Capacità di apprendimento). On completion, the student will be able to understand the structure of unsupervised learning. STATISTICAL LEARNING Knowledge and understanding (Conoscenza e capacità di comprensione). The objectives of the module aim at acquiring knowledge about: i) setting of the learning problem and introducing the general model of the risk functional from empirical data; ii) main statistical learning techniques for regression and data classification. Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, The student will be able: i) to implement main statistical models for supervised and unsupervised learning; ii) to summarize the main features of a dataset and extract knowledge from data properly. Making judgements (Autonomia di giudizio). On completion, students will able how to choose a suitable statistical model, apply sound statistical methods, and perform the analyses using statistical software Communication skills (Abilità comunicative). On completion, students will be able how to present the results from the statistical analyses, and which conclusions can be drawn from the analyses. Learning skills (Capacità di apprendimento). On completion, students will be able to understand the structure of the statistical learning. |
6 | SECS-S/01 | 40 | - | - | - | - | Attività formative caratterizzanti | ENG | ||||||||||||||||||||||||||||
| - STATISTICAL LEARNING | Erogato anche in altro semestre o anno | ||||||||||||||||||||||||||||||||||||
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9793879 -
OPTIMIZATION
(obiettivi)
This graduated-level course introduces analytic tools and optimization methods that are suitable for large-scale problems arising in data science applications. The course presents both basic and advanced concepts of optimization and explores several algorithms that are efficient for network problems.
The student will acquire the ability to formulate, in mathematical terms, problems related to profit maximization and cost minimization, optimization of resources, and traffic network equilibria. The goals of the course are: Knowledge and understanding: the aim of the course is to acquire advanced knowledge that allows students to study optimization problems and model techniques of large-scale decision-making problems. The students will be able to use algorithms for both linear and nonlinear programming problems. Applying knowledge and understanding: students will acquire knowledge useful to identify and model real-life decision-making problems. In addition, through real examples, the student will be able to implement correct solutions for complex problems. Making judgments: students will be able to choose and solve autonomously complex decision-making problems and to interpret the solutions. Communication skills: students will acquire base communication and reading skills using technical language. Learning skills: the course provides students with theoretical and practical methodologies and skills to deal with large-scale optimization problems.
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RACITI Fabio
(programma)
Linear Programming (LP)(about 13 h)
LP models; Graphical method; Simplex method; Duality; Sensitivity analysis Integer Linear Programming (ILP)(about 9 h) Branch & Bound method; 0-1 programming; Knapsack problem Software(about 5 hours) Matlab, Mathematica, Network problems(about 13 h) Graphs (Kruskal, Dijkstra, Bellman-Kalaba algorithms)  J. Stacho, Introduction to Operations Research, Columbia University, NY,http://www.cs.toronto.edu/~stacho/public/IEOR4004-notes1.pdf M.S. Bazaraa, J.J. Jarvis, H.D. Sherali, Linear Programming and Network Flows, John Wiley & Sons, 2009. F. Hillier, G.J. Liebermann, “Introduction to Operations Research”, McGraw-Hill, 2006 |
6 | MAT/09 | 40 | - | - | - | - | Attività formative caratterizzanti | ENG | ||||||||||||||||||||||||||||
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9793940 -
STATISTICAL LABORATORY
(obiettivi)
1.The objectives aim at introducing the knowledge of the R language for statistical data analysis with special focus on descriptive statistics, probability distributions and statistical inference.
2.On completion. Students will be able to utilize the R language for: i) providing basic statistical analyses of data; ii) simulating data according to given probability distributions; iii) applying main methods of statistical inference. 3.On completion, students will able to extract knowledge from data through statistical analyses in R. 4.On completion, students will be able how to present the results from the statistical analyses, based on the use of the statistical software R. 5.On completion, students will able how to utilize the statistical software R for basic data analysis and modeling.
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ORTIS ALESSANDRO
(programma)
Use of the statistical software in R regarding:
Descriptive Statistics. Simple Statistical Distributions. Data tables. Frequency distributions. Main summary statistics: arithmetic mean, geometric mean, harmonic mean. Median and percentiles. Variance, standard deviation, relative variation. Graphical representations. Multiple Statistical Distributions. Contingency Tables. Joint distributions, marginal and conditional distributions. Covariance and correlation. Probability.Random number generation and data modeling according to different probability distributions: uniform, binomial, Poisson, Gaussian. Statistical inference.Sample distributions: Student-t, chi-square. Confidence estimation. Confidence level. Confidence bounds for means, variances, proportions. Hypothesis testing. Null hypotheses and alternative hypotheses. P-values. Statistical tests for means, variances, proportions, comparison of means, comparison of proportions. Statistical models.The simple regression model. Goodness of fit. Residual analysis. Inference on the parameters of a linear regression model. Documents available on the web page of The R Project for Statistical Computing: https://www.r-project.org and other resources available on the web.
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3 | - | - | 36 | - | - | Ulteriori attività formative (art.10, comma 5, lettera d) | ENG | |||||||||||||||||||||||||||||
Secondo semestre
| Insegnamento | CFU | SSD | Ore Lezione | Ore Eserc. | Ore Lab | Ore Altro | Ore Studio | Attività | Lingua |
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9793926 -
DATA BASE AND BIG DATA ANALYTICS
(obiettivi)
The course covers the fundamental concepts of management and design of database systems.
Topics include data models (relational); query languages (SQL); implementation techniques of database management systems (index structures and query processing); and noSQL databases. The learning objectives are: a) To understand and use the main technologies for database management; b) To design a relational database (and not), from a conceptual, logical and physical perspective; c) To use SQL language for performing efficient queries in cases of large datasets; and d) To create and query large scale datasets. |
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| - DATA BASE | Erogato anche in altro semestre o anno | ||||||||
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BIG DATA ANALYTICS
(obiettivi)
he course covers the fundamental concepts of management and design of database systems.
Topics include data models (relational); query languages (SQL); implementation techniques of database management systems (index structures and query processing); and noSQL databases. The learning objectives are: a) To understand and use the main technologies for database management; b) To design a relational database (and not), from a conceptual, logical and physical perspective; c) To use SQL language for performing efficient queries in cases of large datasets; and d) To create and query large scale datasets. |
6 | ING-INF/05 | 40 | - | - | - | - | Attività formative caratterizzanti | ENG |
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9793875 -
DATA ANALYSIS AND STATISTICAL LEARNING
(obiettivi)
1. Knowledge and understanding (Conoscenza e capacità di comprensione). The first “Statistical Learning” module mainly concerns the fundamentals of two of the main methods used in unsupervised learning: principal component analysis and cluster analysis.
2. Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, the student will be able: i) to implement the main methods used in unsupervised learning; ii) to summarize the main features of a dataset and extract knowledge from data properly. 3. Making judgements (Autonomia di giudizio). On completion, the student will be able to choose a suitable statistical model, apply it, and perform the analysis using a statistical software. 4. Communication skills (Abilità comunicative). On completion, the student will be able to present the results from the statistical analysis, and which conclusions can be drawn. 5. Learning skills (Capacità di apprendimento). On completion, the student will be able to understand the structure of unsupervised learning. |
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| - DATA ANALYSIS | Erogato anche in altro semestre o anno | ||||||||
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STATISTICAL LEARNING
(obiettivi)
DATA ANALYSIS
1. Knowledge and understanding (Conoscenza e capacità di comprensione). The first “Statistical Learning” module mainly concerns the fundamentals of two of the main methods used in unsupervised learning: principal component analysis and cluster analysis. 2. Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, the student will be able: i) to implement the main methods used in unsupervised learning; ii) to summarize the main features of a dataset and extract knowledge from data properly. 3. Making judgements (Autonomia di giudizio). On completion, the student will be able to choose a suitable statistical model, apply it, and perform the analysis using a statistical software. 4. Communication skills (Abilità comunicative). On completion, the student will be able to present the results from the statistical analysis, and which conclusions can be drawn. 5. Learning skills (Capacità di apprendimento). On completion, the student will be able to understand the structure of unsupervised learning. STATISTICAL LEARNING Knowledge and understanding (Conoscenza e capacità di comprensione). The objectives of the module aim at acquiring knowledge about: i) setting of the learning problem and introducing the general model of the risk functional from empirical data; ii) main statistical learning techniques for regression and data classification. Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, The student will be able: i) to implement main statistical models for supervised and unsupervised learning; ii) to summarize the main features of a dataset and extract knowledge from data properly. Making judgements (Autonomia di giudizio). On completion, students will able how to choose a suitable statistical model, apply sound statistical methods, and perform the analyses using statistical software Communication skills (Abilità comunicative). On completion, students will be able how to present the results from the statistical analyses, and which conclusions can be drawn from the analyses. Learning skills (Capacità di apprendimento). On completion, students will be able to understand the structure of the statistical learning. |
6 | SECS-S/01 | 40 | - | - | - | - | Attività formative caratterizzanti | ENG |
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9793878 -
BEHAVIORAL ECONOMICS AND COMPLEXITY
(obiettivi)
1.The course aims to build consciousness on the roots of complexity in human behaviors, with reference to their consequences on dynamic perspectives of economic relations. Main contributions of related literature will provide the methodological approach to understand socio-economic relations, by comparing theoretical models and empirical data.
2.The course will analyze both the micro- and the macro-economic perspective, by underlining, respectively, the behavioral approach in the individual choice paradigm and the emergent dynamics in collective phenomena. An essential introduction to agent-based modelling will be given, as one of the most adequate tools of analysis in the field. 3.The course will provide students with adequate abilities to distinguish complex phenomena and to reconcile correct modeling structures with socio-economic problems at hands. 4.The course has an experimental nature. It deals with borderline topics and non-standard approaches for economic analysis. Therefore, a specific effort will be done to help students learning the appropriate terminology and the ability to discuss actual aspects of studied concepts. 5.The course will be a starting point more than a consolidated set of results. All teaching materials, references and presented topics will create a toolbox for many possible future developments, for both further studies and professional applications.
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BIONDO ALESSIO EMANUELE
(programma)
The course aims to present the developments in the context of Micro- and Macroeconomic Theory deriving from the analysis of, respectively, the behavioral approach in the choice paradigm and the emergent dynamics in collective phenomena. Thus, the first part of the course will provide a broad introduction to the concept of rationality in economics and to the advances of modern literature related to the implementation of bounded rationality and behavioral approaches. The second part, instead, will be focused on the concepts of complexity, heterogeneity and interaction in economic models, and special attention will be devoted to the study of macroeconomic agent-based models. For both blocks, a step-by-step introduction of essential ABM building elements will be provided. Finally, a user-friendly open access platform will be presented in order to enable students to build their own models and perform simulations to be confronted with empirical data for policies assessment.
A - Economic Behavior and Microeconomics: Rational choice in Economics and Bounded Rationality; Introduction to Computational Economics. B - Complexity and Macroeconomics: Complexity and Emergent Macroeconomics; Network Economics, Agent-based Modeling. Becker G.S.,The Economic Approach to Human Behavior, The University of Chicago Press, 1976, chapters: 1, 7, 8, 12, 13.
Gilboa I.,Rational Choice, 2010, The MIT Press, Cambridge. Simon H.A., (1955) A Behavioral Model of Rational Choice,The Quarterly Journal of Economics, vol.69, No.1., pp.99-118. Kahneman D., Tversky A., (1979)An Analysis of Decision under Risk,Econometrica, Vol. 47, No. 2 (Mar., 1979), pp. 263-291 Kahneman D., Tversky A., (1984)Choice, Values, Frames,American Psychologist,vol. 39, No. 4, 341-350. Cioffi-RevillaC.,Introduction to Computational Social Science, Springer 2014, chapters: 5, 6, 7. Delli Gatti D., Gaffeo E., Gallegati M., Giulioni G., Palestrini A.,Emergent Macroeconomics, 2008, Springer-Verlag, Milan. Jackson M.O.,Social and Economic Networks, 2010, Princeton University Press, chapters: 2, 3, 6. |
9 | SECS-P/02 | 60 | - | - | - | - | Attività formative caratterizzanti | ENG |
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9793876 -
DIGITAL INNOVATION AND TRANSFORMATION MANAGEMENT
(obiettivi)
Digital Innovation and Transformation Management provides a comprehensive suite of strategy concepts, tools, methods and perspectives to understand and manage your way through a digital transformation and to develop a strategic response to the emerging digital revolution and to then align your organization for effective strategy execution.
Digital Innovation and Transformation Management course is designed to lead and execute digital innovation initiatives and develop new business models for existing and nascent companies in a wide range of industries. Future-proof your organization by leveraging human-centred innovation and the power of digital technologies, to tap into customer insights and to deliver sustained competitive advantage by developing new products and services, entrepreneurial initiatives, innovative start-ups, consulting in strategic, marketing and R&D management. This course serves as the capstone course for the Data science for management program. As a capstone, the goal is to integrate technological and managerial perspectives. The course provides a comprehensive suite of strategy concepts, tools, methods and perspectives to understand and manage your way through a digital transformation and to develop a strategic response to the emerging digital revolution and to then align your organization for effective strategy execution. The course is designed to lead and execute digital innovation initiatives and develop new business models for existing and nascent companies in entrepreneurial ecosystem. Students will be able to learn the key theoretical and conceptual categories (knowledge and understanding) that show an entrepreneurial approach to digital innovation and transformation management. Students will be able to apply professional scheme, models and tools learned using cases studies, also making judgements and contributing to lecture interactions during presentations and discussions in class (communication skills). Students will be able to learn how to learn, since digital innovation generates changes and evolves over time.
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ROMANO Marco
(programma)
DETAILED COURSE CONTENT
The course focuses on economic, strategic and technological dimensions of designing and developing digital solutions and business models to shape and lead technological and social impacts taking place in distributed innovation networks and ecosystems. The managerial perspective emphasis the different organizational role at every level, e.g. project manager, team manager, functional manager, general manager, CEO, Founder, Startupper, that are involved in digital innovation and transformation enhancing the company competitiveness. Currently, organizations with successful transformations deploy more technologies than others developing digital innovation, building managerial capabilities and planning talent development practices in the following critical technological areas of interest of the course: traditional web technologies, cloud-based services, mobile internet technologies, big data and big data architecture (eg, data lakes), internet of things, design thinking, artificial-intelligence tools, robotics (eg, robotic process automation), advanced neural machine-learning techniques (eg, deep learning), augmented-reality technologies, additive manufacturing (eg, 3-D printing). In details the course will help you learn: the business, economic and technological dimensions to manage strategically the digital revolution; the entrepreneurial trade-off between existing business models and new digitally enhanced emphasizing platforms and ecosystems; the competitive collaborations among companies with different digital business models; how to shape and lead innovative solutions (product and service) in the digital contest. An important part of the course are the several happenings and seminars, which will bring together leading practitioners at the cutting edge of digital technologies to discuss emerging issues. TEXTBOOK INFORMATION
Textbook:BRESCIANI S., FERRARIS A., ROMANO M., SANTORO G. (2021). Digital Transformation Management for Agile Organizations. p. 1-216, Howard House, Wagon Lane, Bingley BD16 1WA, UK:Emerald Publishing Limited, ISBN: 9781800431720. Reading Materials will be announced in class and electronically. |
9 | SECS-P/08 | 60 | - | - | - | - | Attività formative caratterizzanti | ENG |
Secondo anno
Primo semestre
| Insegnamento | CFU | SSD | Ore Lezione | Ore Eserc. | Ore Lab | Ore Altro | Ore Studio | Attività | Lingua | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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9793877 -
ADVANCED MACHINE LEARNING AND KNOWLEDGE DISCOVERY
(obiettivi)
ADVANCED MACHINE LEARNING
The module will focus on the implementations of various machine learning techniques and their applications in various domains. The primary tools used in the class are the Python programming language and several associated libraries. KNOWLEDGE DISCOVERY This module covers the fundamental concepts of deep learning methods and how to use them for extracting, modelling and visualizing the learned knowledge. Topics include: neural networks with backpropagation, convolutional neural networks, recurrent neural networks, methods for representation learning, and how to use them under different learning regimes (supervised, unsupervised and reinforcement learning) and in variety of real-world applications ranging from computer vision, machine translation and medical image analysis. The learning objectives are: to understand and use the main methodologies and techniques for learning from data to understand the main methodologies to design and implement neural networks for real-world applications to understand how to extract and learn knowledge in scenarios when supervision cannot be provided to understand and foresee the reliability of machine learning methods in operational scenarios. Knowledge and understanding To understand the main concepts of learning from data To understand concepts and tools for building intelligent systems using supervision and no supervision To understand the most important machine learning and artificial intelligence methodologies and techniques used by industries to make sense of data in order to support the decision process To understand what are the most appropriate techniques to be used in different real-world applications Applying knowledge and understanding To be able to effectively understand and use the main tools for creating, loading and manipulating datasets. To design and implement from scratch a machine learning system following application-derived constraints in terms of modelling and data To understand proper benchmarks and baselines and analysing achieved results and their generalization in real-world applications To be able to apply methodologies and techniques to analyse data. |
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ADVANCED MACHINE LEARNING
(obiettivi)
ADVANCED MACHINE LEARNING
The module will focus on the implementations of various machine learning techniques and their applications in various domains. The primary tools used in the class are the Python programming language and several associated libraries. KNOWLEDGE DISCOVERY This module covers the fundamental concepts of deep learning methods and how to use them for extracting, modelling and visualizing the learned knowledge. Topics include: neural networks with backpropagation, convolutional neural networks, recurrent neural networks, methods for representation learning, and how to use them under different learning regimes (supervised, unsupervised and reinforcement learning) and in variety of real-world applications ranging from computer vision, machine translation and medical image analysis. The learning objectives are: to understand and use the main methodologies and techniques for learning from data to understand the main methodologies to design and implement neural networks for real-world applications to understand how to extract and learn knowledge in scenarios when supervision cannot be provided to understand and foresee the reliability of machine learning methods in operational scenarios. Knowledge and understanding To understand the main concepts of learning from data To understand concepts and tools for building intelligent systems using supervision and no supervision To understand the most important machine learning and artificial intelligence methodologies and techniques used by industries to make sense of data in order to support the decision process To understand what are the most appropriate techniques to be used in different real-world applications Applying knowledge and understanding To be able to effectively understand and use the main tools for creating, loading and manipulating datasets. To design and implement from scratch a machine learning system following application-derived constraints in terms of modelling and data To understand proper benchmarks and baselines and analysing achieved results and their generalization in real-world applications To be able to apply methodologies and techniques to analyse data. |
6 | INF/01 | 40 | - | - | - | - | Attività formative caratterizzanti | ENG | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| - KNOWLEDGE DISCOVERY | Erogato anche in altro semestre o anno | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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9793880 -
COMPUTER SECURITY AND DATA PROTECTION
(obiettivi)
Nowadays data controllers must design information systems that provide the highest possible privacy guarantees. A fundamental enabler to achieve this is cryptography.
This class is intended to provide an introduction to the main concepts of modern cryptography and their usage to protect data e build secure systems. The main focus will be on constructions of various building blocks, such as encryption schemes, message authentication codes and digital signatures. We will try to understand what properties we expect from these objects, how to define these properties and how to construct schemes that realize them. We will also focus on schemes that are widely used in practice. These include, for instance, AES, SHA, HMAC and RSA. However, rather than using these tools as black box, we will show how they are built and the security level they provide. No programming will be required for this class. The goals of this course, in terms of expected results, are Knowledge and understanding (Conoscenza e capacità di comprensione). Students will learn the fundamental ideas and principles underlying modern cryptography and modern secure systems. Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, the student will be able to securely use cryptographic tools like encryption schema and digital signatures and to understand their exact role in secure systems. Making judgements (Autonomia di giudizio). By studying concrete examples and common mistakes students will learn how to use solutions that providee high security guarantees. Communication skills (Abilità comunicative). On completion, students will acquire communication skills that will allow them to fluently communicate using the technical language of computer security. Learning skills (Capacità di apprendimento). On completion, students will acquire methodologies that will allow them to securely deal with problems that require the usage of secure solutions.
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CATALANO Dario
(programma)
Introduction to the main ideas of this class.
Source: Cap 1 from [1] A look back: Classical Ciphers and One Time Pad. Shift cipher and substitution cipher. Cryptanalysis of the substitution cipher. Perfect Security. The substitution cipher does not guarantee perfect security. One time pad. One time pad provides perfect perfect security. Source: Cap 2 from [1] Block Ciphers – AESThe blockcipher Rijndael. Pseudorandom functions and relations to block ciphers. AES in practice. Birthday Paradox. Source: Cap 3,4 from [1] Symmetric encryption: Modes of operation. ECB, CBC$, CTRC and CTR$. Security notions for symmetric encryption. Source: Cap 5 from [1] Integrity and Hash functions. Collision resistant hash functions. Generic attacks to collision resistance. SHA3. Source: Cap 6 from [1] Message Authentication. Notion of security for MACs. The PRF as a MAC paradigm. CBC-MAC. HMAC. Source: Cap 7 from [1] Intro to asymmetric cryptography. One way functions and Trapdoor (one-way) functions. Number theory basics. Discrete logarithms. Computation Diffie Hellman problem and Key Exchange. Factoring and RSA. Source: Cap 9, 10 from [1], relevant parts from [2] Asymmetric encryption. Notions of security for asymmetric cryptosystems. The El-Gamal encryption scheme. Homomorphic Encryption (basics). RSA-OAEP. Source: Cap 11 from [1] and slides Digital Signatures. A notion of security for digital signatures. The Hash then invert paradigm for digital signatures. Digital Signatures in practice. Source: Cap 12 from [1]. Bonus Application: Bitcoin Source: Slides and Chapter 2 of [4] [1] M. Bellare, P. Rogaway “Introduction to Modern Cryptography” Scaricabile da http://www.cs.ucsd.edu/~mihir/cse107/classnotes.html
[2] V. Shoup A Computational Introduction to Number Theory and Algebra Scaricabile da http://shoup.net/ntb/ [3] J. Katz, Y. Lindell “Introduction to Modern Cryptography” CRC press [4]A. Miller, A. Narayanan, E. Felten, J. Bonneau, and S. Goldfeder “Bitcoin and Cryptocurrency Technologies: A Comprehensive Introduction”. Princeton University Press. |
6 | INF/01 | 40 | - | - | - | - | Attività formative caratterizzanti | ENG | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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9793881 -
NEURAL COMPUTING
(obiettivi)
The course covers the theory and practice of artificial neural networks, highlighting their relevance both for artificial intelligence applications and for modeling human cognition and brain function. Theoretical discussion of various types of neural networks and learning algorithms is complemented by hands-on practices in the computer lab. Models for classification and regression, as well as neural network architectures (e.g., Deep Learning) will be discussed. The course will present the techniques to design and optimize learning algorithms, and those useful to assess the performance of Machine Learning systems.
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BATTIATO SEBASTIANO
(programma)
Linear Models for Regression: Linear Models for Classification: Gradient Descent, Multi-Class Classification, Classifiers Evaluation
Neural models and Network Architectures Basic neural network models: multilayer perceptron, distance or similarity based neural networks, associative memory and self-organizing feature map, radial basis function based multilayer perceptron, neural network decision trees, etc. Basic learning algorithms: the delta learning rule, the back propagation algorithm, self-organization learning, etc. Supervised Learning with Neural Networks Deep Learning: Convolutional Neural Network Python programming and Python Libraries for Machine Learning DEEP LEARNING FROM BASICS TO PRACTICE (2020)
https://www.glassner.com/portfolio/deep-learning-from-basics-to-practice/ Dive into Deep Learning (2020) https://d2l.ai/d2l-en.pdf OTHER E. Alpaydin, “Introduction to Machine Learning”, MIT Press, 2014 I. Goodfellow, Y. Bengio and A. Courville, "Deep Learning", MIT Press, 2016 M. P. Deisenroth, A A. Faisal, and C. Soon On, Mathematics for Machine Learning, MIT Press, 2019 |
6 | INF/01 | 40 | - | - | - | - | Attività formative caratterizzanti | ENG | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Secondo semestre
| Insegnamento | CFU | SSD | Ore Lezione | Ore Eserc. | Ore Lab | Ore Altro | Ore Studio | Attività | Lingua | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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9793877 -
ADVANCED MACHINE LEARNING AND KNOWLEDGE DISCOVERY
(obiettivi)
ADVANCED MACHINE LEARNING
The module will focus on the implementations of various machine learning techniques and their applications in various domains. The primary tools used in the class are the Python programming language and several associated libraries. KNOWLEDGE DISCOVERY This module covers the fundamental concepts of deep learning methods and how to use them for extracting, modelling and visualizing the learned knowledge. Topics include: neural networks with backpropagation, convolutional neural networks, recurrent neural networks, methods for representation learning, and how to use them under different learning regimes (supervised, unsupervised and reinforcement learning) and in variety of real-world applications ranging from computer vision, machine translation and medical image analysis. The learning objectives are: to understand and use the main methodologies and techniques for learning from data to understand the main methodologies to design and implement neural networks for real-world applications to understand how to extract and learn knowledge in scenarios when supervision cannot be provided to understand and foresee the reliability of machine learning methods in operational scenarios. Knowledge and understanding To understand the main concepts of learning from data To understand concepts and tools for building intelligent systems using supervision and no supervision To understand the most important machine learning and artificial intelligence methodologies and techniques used by industries to make sense of data in order to support the decision process To understand what are the most appropriate techniques to be used in different real-world applications Applying knowledge and understanding To be able to effectively understand and use the main tools for creating, loading and manipulating datasets. To design and implement from scratch a machine learning system following application-derived constraints in terms of modelling and data To understand proper benchmarks and baselines and analysing achieved results and their generalization in real-world applications To be able to apply methodologies and techniques to analyse data. |
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| - ADVANCED MACHINE LEARNING | Erogato anche in altro semestre o anno | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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KNOWLEDGE DISCOVERY
(obiettivi)
ADVANCED MACHINE LEARNING
The module will focus on the implementations of various machine learning techniques and their applications in various domains. The primary tools used in the class are the Python programming language and several associated libraries. KNOWLEDGE DISCOVERY This module covers the fundamental concepts of deep learning methods and how to use them for extracting, modelling and visualizing the learned knowledge. Topics include: neural networks with backpropagation, convolutional neural networks, recurrent neural networks, methods for representation learning, and how to use them under different learning regimes (supervised, unsupervised and reinforcement learning) and in variety of real-world applications ranging from computer vision, machine translation and medical image analysis. The learning objectives are: to understand and use the main methodologies and techniques for learning from data to understand the main methodologies to design and implement neural networks for real-world applications to understand how to extract and learn knowledge in scenarios when supervision cannot be provided to understand and foresee the reliability of machine learning methods in operational scenarios. Knowledge and understanding To understand the main concepts of learning from data To understand concepts and tools for building intelligent systems using supervision and no supervision To understand the most important machine learning and artificial intelligence methodologies and techniques used by industries to make sense of data in order to support the decision process To understand what are the most appropriate techniques to be used in different real-world applications Applying knowledge and understanding To be able to effectively understand and use the main tools for creating, loading and manipulating datasets. To design and implement from scratch a machine learning system following application-derived constraints in terms of modelling and data To understand proper benchmarks and baselines and analysing achieved results and their generalization in real-world applications To be able to apply methodologies and techniques to analyse data. |
6 | ING-INF/05 | 40 | - | - | - | - | Attività formative caratterizzanti | ENG | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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9793963 -
ELECTIVE COURSE
(obiettivi)
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12 | 80 | - | - | - | - | Attività formative a scelta dello studente (art.10, comma 5, lettera a) | ENG | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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9795513 -
Final dissertation
(obiettivi)
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Research for and writing of the final dissertation
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10 | - | - | - | 250 | - | Per la prova finale e la lingua straniera (art.10, comma 5, lettera c) | ITA | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Presentation and discussion of the final dissertation
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2 | - | - | - | 50 | - | Per la prova finale e la lingua straniera (art.10, comma 5, lettera c) | ITA | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Data driven applications for IoT
Primo anno
Primo semestre
| Insegnamento | CFU | SSD | Ore Lezione | Ore Eserc. | Ore Lab | Ore Altro | Ore Studio | Attività | Lingua | ||||||||||||||||||||||||||||
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9793926 -
DATA BASE AND BIG DATA ANALYTICS
(obiettivi)
The course covers the fundamental concepts of management and design of database systems.
Topics include data models (relational); query languages (SQL); implementation techniques of database management systems (index structures and query processing); and noSQL databases. The learning objectives are: a) To understand and use the main technologies for database management; b) To design a relational database (and not), from a conceptual, logical and physical perspective; c) To use SQL language for performing efficient queries in cases of large datasets; and d) To create and query large scale datasets. |
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DATA BASE
(obiettivi)
The course covers the fundamental concepts of management and design of database systems.
Topics include data models (relational); query languages (SQL); implementation techniques of database management systems (index structures and query processing); and noSQL databases. The learning objectives are: a) To understand and use the main technologies for database management; b) To design a relational database (and not), from a conceptual, logical and physical perspective; c) To use SQL language for performing efficient queries in cases of large datasets; and d) To create and query large scale datasets. |
6 | ING-INF/05 | 40 | - | - | - | - | Attività formative caratterizzanti | ENG | ||||||||||||||||||||||||||||
| - BIG DATA ANALYTICS | Erogato anche in altro semestre o anno | ||||||||||||||||||||||||||||||||||||
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9793875 -
DATA ANALYSIS AND STATISTICAL LEARNING
(obiettivi)
1. Knowledge and understanding (Conoscenza e capacità di comprensione). The first “Statistical Learning” module mainly concerns the fundamentals of two of the main methods used in unsupervised learning: principal component analysis and cluster analysis.
2. Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, the student will be able: i) to implement the main methods used in unsupervised learning; ii) to summarize the main features of a dataset and extract knowledge from data properly. 3. Making judgements (Autonomia di giudizio). On completion, the student will be able to choose a suitable statistical model, apply it, and perform the analysis using a statistical software. 4. Communication skills (Abilità comunicative). On completion, the student will be able to present the results from the statistical analysis, and which conclusions can be drawn. 5. Learning skills (Capacità di apprendimento). On completion, the student will be able to understand the structure of unsupervised learning. |
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DATA ANALYSIS
(obiettivi)
DATA ANALYSIS
1. Knowledge and understanding (Conoscenza e capacità di comprensione). The first “Statistical Learning” module mainly concerns the fundamentals of two of the main methods used in unsupervised learning: principal component analysis and cluster analysis. 2. Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, the student will be able: i) to implement the main methods used in unsupervised learning; ii) to summarize the main features of a dataset and extract knowledge from data properly. 3. Making judgements (Autonomia di giudizio). On completion, the student will be able to choose a suitable statistical model, apply it, and perform the analysis using a statistical software. 4. Communication skills (Abilità comunicative). On completion, the student will be able to present the results from the statistical analysis, and which conclusions can be drawn. 5. Learning skills (Capacità di apprendimento). On completion, the student will be able to understand the structure of unsupervised learning. STATISTICAL LEARNING Knowledge and understanding (Conoscenza e capacità di comprensione). The objectives of the module aim at acquiring knowledge about: i) setting of the learning problem and introducing the general model of the risk functional from empirical data; ii) main statistical learning techniques for regression and data classification. Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, The student will be able: i) to implement main statistical models for supervised and unsupervised learning; ii) to summarize the main features of a dataset and extract knowledge from data properly. Making judgements (Autonomia di giudizio). On completion, students will able how to choose a suitable statistical model, apply sound statistical methods, and perform the analyses using statistical software Communication skills (Abilità comunicative). On completion, students will be able how to present the results from the statistical analyses, and which conclusions can be drawn from the analyses. Learning skills (Capacità di apprendimento). On completion, students will be able to understand the structure of the statistical learning. |
6 | SECS-S/01 | 40 | - | - | - | - | Attività formative caratterizzanti | ENG | ||||||||||||||||||||||||||||
| - STATISTICAL LEARNING | Erogato anche in altro semestre o anno | ||||||||||||||||||||||||||||||||||||
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9793879 -
OPTIMIZATION
(obiettivi)
This graduated-level course introduces analytic tools and optimization methods that are suitable for large-scale problems arising in data science applications. The course presents both basic and advanced concepts of optimization and explores several algorithms that are efficient for network problems.
The student will acquire the ability to formulate, in mathematical terms, problems related to profit maximization and cost minimization, optimization of resources, and traffic network equilibria. The goals of the course are: Knowledge and understanding: the aim of the course is to acquire advanced knowledge that allows students to study optimization problems and model techniques of large-scale decision-making problems. The students will be able to use algorithms for both linear and nonlinear programming problems. Applying knowledge and understanding: students will acquire knowledge useful to identify and model real-life decision-making problems. In addition, through real examples, the student will be able to implement correct solutions for complex problems. Making judgments: students will be able to choose and solve autonomously complex decision-making problems and to interpret the solutions. Communication skills: students will acquire base communication and reading skills using technical language. Learning skills: the course provides students with theoretical and practical methodologies and skills to deal with large-scale optimization problems.
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RACITI Fabio
(programma)
Linear Programming (LP)(about 13 h)
LP models; Graphical method; Simplex method; Duality; Sensitivity analysis Integer Linear Programming (ILP)(about 9 h) Branch & Bound method; 0-1 programming; Knapsack problem Software(about 5 hours) Matlab, Mathematica, Network problems(about 13 h) Graphs (Kruskal, Dijkstra, Bellman-Kalaba algorithms) J. Stacho, Introduction to Operations Research, Columbia University, NY,http://www.cs.toronto.edu/~stacho/public/IEOR4004-notes1.pdf M.S. Bazaraa, J.J. Jarvis, H.D. Sherali, Linear Programming and Network Flows, John Wiley & Sons, 2009. F. Hillier, G.J. Liebermann, “Introduction to Operations Research”, McGraw-Hill, 2006 |
6 | MAT/09 | 40 | - | - | - | - | Attività formative caratterizzanti | ENG | ||||||||||||||||||||||||||||
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9793940 -
STATISTICAL LABORATORY
(obiettivi)
1.The objectives aim at introducing the knowledge of the R language for statistical data analysis with special focus on descriptive statistics, probability distributions and statistical inference.
2.On completion. Students will be able to utilize the R language for: i) providing basic statistical analyses of data; ii) simulating data according to given probability distributions; iii) applying main methods of statistical inference. 3.On completion, students will able to extract knowledge from data through statistical analyses in R. 4.On completion, students will be able how to present the results from the statistical analyses, based on the use of the statistical software R. 5.On completion, students will able how to utilize the statistical software R for basic data analysis and modeling.
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ORTIS ALESSANDRO
(programma)
Use of the statistical software in R regarding:
Descriptive Statistics. Simple Statistical Distributions. Data tables. Frequency distributions. Main summary statistics: arithmetic mean, geometric mean, harmonic mean. Median and percentiles. Variance, standard deviation, relative variation. Graphical representations. Multiple Statistical Distributions. Contingency Tables. Joint distributions, marginal and conditional distributions. Covariance and correlation. Probability.Random number generation and data modeling according to different probability distributions: uniform, binomial, Poisson, Gaussian. Statistical inference.Sample distributions: Student-t, chi-square. Confidence estimation. Confidence level. Confidence bounds for means, variances, proportions. Hypothesis testing. Null hypotheses and alternative hypotheses. P-values. Statistical tests for means, variances, proportions, comparison of means, comparison of proportions. Statistical models.The simple regression model. Goodness of fit. Residual analysis. Inference on the parameters of a linear regression model. Documents available on the web page of The R Project for Statistical Computing: https://www.r-project.org and other resources available on the web.
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3 | - | - | 36 | - | - | Ulteriori attività formative (art.10, comma 5, lettera d) | ENG | |||||||||||||||||||||||||||||
Secondo semestre
| Insegnamento | CFU | SSD | Ore Lezione | Ore Eserc. | Ore Lab | Ore Altro | Ore Studio | Attività | Lingua |
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9793926 -
DATA BASE AND BIG DATA ANALYTICS
(obiettivi)
The course covers the fundamental concepts of management and design of database systems.
Topics include data models (relational); query languages (SQL); implementation techniques of database management systems (index structures and query processing); and noSQL databases. The learning objectives are: a) To understand and use the main technologies for database management; b) To design a relational database (and not), from a conceptual, logical and physical perspective; c) To use SQL language for performing efficient queries in cases of large datasets; and d) To create and query large scale datasets. |
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| - DATA BASE | Erogato anche in altro semestre o anno | ||||||||
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BIG DATA ANALYTICS
(obiettivi)
he course covers the fundamental concepts of management and design of database systems.
Topics include data models (relational); query languages (SQL); implementation techniques of database management systems (index structures and query processing); and noSQL databases. The learning objectives are: a) To understand and use the main technologies for database management; b) To design a relational database (and not), from a conceptual, logical and physical perspective; c) To use SQL language for performing efficient queries in cases of large datasets; and d) To create and query large scale datasets. |
6 | ING-INF/05 | 40 | - | - | - | - | Attività formative caratterizzanti | ENG |
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9793875 -
DATA ANALYSIS AND STATISTICAL LEARNING
(obiettivi)
1. Knowledge and understanding (Conoscenza e capacità di comprensione). The first “Statistical Learning” module mainly concerns the fundamentals of two of the main methods used in unsupervised learning: principal component analysis and cluster analysis.
2. Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, the student will be able: i) to implement the main methods used in unsupervised learning; ii) to summarize the main features of a dataset and extract knowledge from data properly. 3. Making judgements (Autonomia di giudizio). On completion, the student will be able to choose a suitable statistical model, apply it, and perform the analysis using a statistical software. 4. Communication skills (Abilità comunicative). On completion, the student will be able to present the results from the statistical analysis, and which conclusions can be drawn. 5. Learning skills (Capacità di apprendimento). On completion, the student will be able to understand the structure of unsupervised learning. |
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| - DATA ANALYSIS | Erogato anche in altro semestre o anno | ||||||||
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STATISTICAL LEARNING
(obiettivi)
DATA ANALYSIS
1. Knowledge and understanding (Conoscenza e capacità di comprensione). The first “Statistical Learning” module mainly concerns the fundamentals of two of the main methods used in unsupervised learning: principal component analysis and cluster analysis. 2. Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, the student will be able: i) to implement the main methods used in unsupervised learning; ii) to summarize the main features of a dataset and extract knowledge from data properly. 3. Making judgements (Autonomia di giudizio). On completion, the student will be able to choose a suitable statistical model, apply it, and perform the analysis using a statistical software. 4. Communication skills (Abilità comunicative). On completion, the student will be able to present the results from the statistical analysis, and which conclusions can be drawn. 5. Learning skills (Capacità di apprendimento). On completion, the student will be able to understand the structure of unsupervised learning. STATISTICAL LEARNING Knowledge and understanding (Conoscenza e capacità di comprensione). The objectives of the module aim at acquiring knowledge about: i) setting of the learning problem and introducing the general model of the risk functional from empirical data; ii) main statistical learning techniques for regression and data classification. Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, The student will be able: i) to implement main statistical models for supervised and unsupervised learning; ii) to summarize the main features of a dataset and extract knowledge from data properly. Making judgements (Autonomia di giudizio). On completion, students will able how to choose a suitable statistical model, apply sound statistical methods, and perform the analyses using statistical software Communication skills (Abilità comunicative). On completion, students will be able how to present the results from the statistical analyses, and which conclusions can be drawn from the analyses. Learning skills (Capacità di apprendimento). On completion, students will be able to understand the structure of the statistical learning. |
6 | SECS-S/01 | 40 | - | - | - | - | Attività formative caratterizzanti | ENG |
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9793878 -
BEHAVIORAL ECONOMICS AND COMPLEXITY
(obiettivi)
1.The course aims to build consciousness on the roots of complexity in human behaviors, with reference to their consequences on dynamic perspectives of economic relations. Main contributions of related literature will provide the methodological approach to understand socio-economic relations, by comparing theoretical models and empirical data.
2.The course will analyze both the micro- and the macro-economic perspective, by underlining, respectively, the behavioral approach in the individual choice paradigm and the emergent dynamics in collective phenomena. An essential introduction to agent-based modelling will be given, as one of the most adequate tools of analysis in the field. 3.The course will provide students with adequate abilities to distinguish complex phenomena and to reconcile correct modeling structures with socio-economic problems at hands. 4.The course has an experimental nature. It deals with borderline topics and non-standard approaches for economic analysis. Therefore, a specific effort will be done to help students learning the appropriate terminology and the ability to discuss actual aspects of studied concepts. 5.The course will be a starting point more than a consolidated set of results. All teaching materials, references and presented topics will create a toolbox for many possible future developments, for both further studies and professional applications.
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BIONDO ALESSIO EMANUELE
(programma)
The course aims to present the developments in the context of Micro- and Macroeconomic Theory deriving from the analysis of, respectively, the behavioral approach in the choice paradigm and the emergent dynamics in collective phenomena. Thus, the first part of the course will provide a broad introduction to the concept of rationality in economics and to the advances of modern literature related to the implementation of bounded rationality and behavioral approaches. The second part, instead, will be focused on the concepts of complexity, heterogeneity and interaction in economic models, and special attention will be devoted to the study of macroeconomic agent-based models. For both blocks, a step-by-step introduction of essential ABM building elements will be provided. Finally, a user-friendly open access platform will be presented in order to enable students to build their own models and perform simulations to be confronted with empirical data for policies assessment.
A - Economic Behavior and Microeconomics: Rational choice in Economics and Bounded Rationality; Introduction to Computational Economics. B - Complexity and Macroeconomics: Complexity and Emergent Macroeconomics; Network Economics, Agent-based Modeling. Becker G.S.,The Economic Approach to Human Behavior, The University of Chicago Press, 1976, chapters: 1, 7, 8, 12, 13.
Gilboa I.,Rational Choice, 2010, The MIT Press, Cambridge. Simon H.A., (1955) A Behavioral Model of Rational Choice,The Quarterly Journal of Economics, vol.69, No.1., pp.99-118. Kahneman D., Tversky A., (1979)An Analysis of Decision under Risk,Econometrica, Vol. 47, No. 2 (Mar., 1979), pp. 263-291 Kahneman D., Tversky A., (1984)Choice, Values, Frames,American Psychologist,vol. 39, No. 4, 341-350. Cioffi-RevillaC.,Introduction to Computational Social Science, Springer 2014, chapters: 5, 6, 7. Delli Gatti D., Gaffeo E., Gallegati M., Giulioni G., Palestrini A.,Emergent Macroeconomics, 2008, Springer-Verlag, Milan. Jackson M.O.,Social and Economic Networks, 2010, Princeton University Press, chapters: 2, 3, 6. |
9 | SECS-P/02 | 60 | - | - | - | - | Attività formative caratterizzanti | ENG |
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9793876 -
DIGITAL INNOVATION AND TRANSFORMATION MANAGEMENT
(obiettivi)
Digital Innovation and Transformation Management provides a comprehensive suite of strategy concepts, tools, methods and perspectives to understand and manage your way through a digital transformation and to develop a strategic response to the emerging digital revolution and to then align your organization for effective strategy execution.
Digital Innovation and Transformation Management course is designed to lead and execute digital innovation initiatives and develop new business models for existing and nascent companies in a wide range of industries. Future-proof your organization by leveraging human-centred innovation and the power of digital technologies, to tap into customer insights and to deliver sustained competitive advantage by developing new products and services, entrepreneurial initiatives, innovative start-ups, consulting in strategic, marketing and R&D management. This course serves as the capstone course for the Data science for management program. As a capstone, the goal is to integrate technological and managerial perspectives. The course provides a comprehensive suite of strategy concepts, tools, methods and perspectives to understand and manage your way through a digital transformation and to develop a strategic response to the emerging digital revolution and to then align your organization for effective strategy execution. The course is designed to lead and execute digital innovation initiatives and develop new business models for existing and nascent companies in entrepreneurial ecosystem. Students will be able to learn the key theoretical and conceptual categories (knowledge and understanding) that show an entrepreneurial approach to digital innovation and transformation management. Students will be able to apply professional scheme, models and tools learned using cases studies, also making judgements and contributing to lecture interactions during presentations and discussions in class (communication skills). Students will be able to learn how to learn, since digital innovation generates changes and evolves over time.
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ROMANO Marco
(programma)
DETAILED COURSE CONTENT
The course focuses on economic, strategic and technological dimensions of designing and developing digital solutions and business models to shape and lead technological and social impacts taking place in distributed innovation networks and ecosystems. The managerial perspective emphasis the different organizational role at every level, e.g. project manager, team manager, functional manager, general manager, CEO, Founder, Startupper, that are involved in digital innovation and transformation enhancing the company competitiveness. Currently, organizations with successful transformations deploy more technologies than others developing digital innovation, building managerial capabilities and planning talent development practices in the following critical technological areas of interest of the course: traditional web technologies, cloud-based services, mobile internet technologies, big data and big data architecture (eg, data lakes), internet of things, design thinking, artificial-intelligence tools, robotics (eg, robotic process automation), advanced neural machine-learning techniques (eg, deep learning), augmented-reality technologies, additive manufacturing (eg, 3-D printing). In details the course will help you learn: the business, economic and technological dimensions to manage strategically the digital revolution; the entrepreneurial trade-off between existing business models and new digitally enhanced emphasizing platforms and ecosystems; the competitive collaborations among companies with different digital business models; how to shape and lead innovative solutions (product and service) in the digital contest. An important part of the course are the several happenings and seminars, which will bring together leading practitioners at the cutting edge of digital technologies to discuss emerging issues. TEXTBOOK INFORMATION
Textbook:BRESCIANI S., FERRARIS A., ROMANO M., SANTORO G. (2021). Digital Transformation Management for Agile Organizations. p. 1-216, Howard House, Wagon Lane, Bingley BD16 1WA, UK:Emerald Publishing Limited, ISBN: 9781800431720. Reading Materials will be announced in class and electronically. |
9 | SECS-P/08 | 60 | - | - | - | - | Attività formative caratterizzanti | ENG |
Secondo anno
Primo semestre
| Insegnamento | CFU | SSD | Ore Lezione | Ore Eserc. | Ore Lab | Ore Altro | Ore Studio | Attività | Lingua | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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9793877 -
ADVANCED MACHINE LEARNING AND KNOWLEDGE DISCOVERY
(obiettivi)
ADVANCED MACHINE LEARNING
The module will focus on the implementations of various machine learning techniques and their applications in various domains. The primary tools used in the class are the Python programming language and several associated libraries. KNOWLEDGE DISCOVERY This module covers the fundamental concepts of deep learning methods and how to use them for extracting, modelling and visualizing the learned knowledge. Topics include: neural networks with backpropagation, convolutional neural networks, recurrent neural networks, methods for representation learning, and how to use them under different learning regimes (supervised, unsupervised and reinforcement learning) and in variety of real-world applications ranging from computer vision, machine translation and medical image analysis. The learning objectives are: to understand and use the main methodologies and techniques for learning from data to understand the main methodologies to design and implement neural networks for real-world applications to understand how to extract and learn knowledge in scenarios when supervision cannot be provided to understand and foresee the reliability of machine learning methods in operational scenarios. Knowledge and understanding To understand the main concepts of learning from data To understand concepts and tools for building intelligent systems using supervision and no supervision To understand the most important machine learning and artificial intelligence methodologies and techniques used by industries to make sense of data in order to support the decision process To understand what are the most appropriate techniques to be used in different real-world applications Applying knowledge and understanding To be able to effectively understand and use the main tools for creating, loading and manipulating datasets. To design and implement from scratch a machine learning system following application-derived constraints in terms of modelling and data To understand proper benchmarks and baselines and analysing achieved results and their generalization in real-world applications To be able to apply methodologies and techniques to analyse data. |
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ADVANCED MACHINE LEARNING
(obiettivi)
ADVANCED MACHINE LEARNING
The module will focus on the implementations of various machine learning techniques and their applications in various domains. The primary tools used in the class are the Python programming language and several associated libraries. KNOWLEDGE DISCOVERY This module covers the fundamental concepts of deep learning methods and how to use them for extracting, modelling and visualizing the learned knowledge. Topics include: neural networks with backpropagation, convolutional neural networks, recurrent neural networks, methods for representation learning, and how to use them under different learning regimes (supervised, unsupervised and reinforcement learning) and in variety of real-world applications ranging from computer vision, machine translation and medical image analysis. The learning objectives are: to understand and use the main methodologies and techniques for learning from data to understand the main methodologies to design and implement neural networks for real-world applications to understand how to extract and learn knowledge in scenarios when supervision cannot be provided to understand and foresee the reliability of machine learning methods in operational scenarios. Knowledge and understanding To understand the main concepts of learning from data To understand concepts and tools for building intelligent systems using supervision and no supervision To understand the most important machine learning and artificial intelligence methodologies and techniques used by industries to make sense of data in order to support the decision process To understand what are the most appropriate techniques to be used in different real-world applications Applying knowledge and understanding To be able to effectively understand and use the main tools for creating, loading and manipulating datasets. To design and implement from scratch a machine learning system following application-derived constraints in terms of modelling and data To understand proper benchmarks and baselines and analysing achieved results and their generalization in real-world applications To be able to apply methodologies and techniques to analyse data. |
6 | INF/01 | 40 | - | - | - | - | Attività formative caratterizzanti | ENG | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| - KNOWLEDGE DISCOVERY | Erogato anche in altro semestre o anno | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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9793927 -
IoT AND BIG DATA FOR SMART SPACES
(obiettivi)
Knowledge and Understanding: On completion of the course, the student shall 1) Know the key technological components underpinning IoT, 2) Understand IoT Architectures and the application of IoT in various domains, 3) Know the difference among networking protocols in the context of resource-constrained IoT devices, and 4) Know how Big Data can be exploited in the context of Smart Spaces.
Applying Knowledge and Understanding: On completion of the course, the student shall be able to analyze and select the appropriate technological solutions for Smart Spaces enabled by IoT and Big Data collection and analysis. Making Judgements: Completing the course, the student will be able to judge the suitability, the capabilities, and the limitations of IoT based applications in the context of Smart Spaces. Further, the student will be able to identify issues, problems, or misleading results. Communication Skills: On completion of the course, the student will be able to illustrate the theoretical and technical properties which characterize IoT based Smart Environments. The student will be able to interact and collaborate with peers and experts in the realization of a project or research. Learning Skills: On completion of the course, the student will be able to autonomously extend the knowledge acquired during the study course by reading and understanding scientific and technical documentation.
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PALESI MAURIZIO
(programma)
Introduction to IoT Systems
Definition of the Internet of Things (IoT): IoT examples; IoT devices; IoT devices vs. computers. Trends in the Adoption of the Internet of Things (IoT): Trends; Potentiality and pervasiveness. The Importance of the Internet of Things (IoT) in Society: Societal benefits of IoT; Risk, privacy and security. IoT Components and Protocols Features and Constraints of Embedded Systems: What are embedded systems; Generic embedded systems structure; Main components overview; Specific components examples; Microcontrollers, Sensor and Actuators; Analog/Digital conversion. Machine-to-Machine (M2M) communication: Technologies for WPAN (BLE, IEEE 802.15.4, etc.); Technologies for WLAN and LPWAN (LoRA and SigFox). IoT application protocols: Requirements, resource constrained protocols, XMPP, CoAP, MQTT, AMQP, WebSocket, etc. IoT Data Storage, Analytics and Platforms for System Integration Architectures for IoT data storage and processing: cloud/fog/edge computing. Hands-on with KNIMEData Analytics Platform IoT Applications Domains Smart space enabled application domains: examples and case studies Introduction to Domain Specific Accelerators for IoT All the teaching material will be made available through the course page on Studium.
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6 | ING-INF/05 | 40 | - | - | - | - | Attività formative caratterizzanti | ENG | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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9793928 -
BIG DATA FOR SMART MANUFACTURING
(obiettivi)
Knowledge and understanding. On completion of the course, the student shall 1) know the basic principles of the smart manufacturing according to the novel IC technologies adopted in the modern industry, and 2) understand methodologies and techniques used in industries to realise the “Failure Prediction and Predictive Maintenance” .
Applying knowledge and understanding. On completion of the course, the student will be able to select the appropriate technological solutions in predictive maintenance. Making judgements. On completion of the course, the student will be able to choose a suitable data science model for each of the subjects treated inside the course. Communication skills. On completion of the course, the student can communicate his conclusions and recommendations about data science applications in smart manufacturing with the argumentation of the knowledge and rationale underpinning these, to both specialist and non-specialist audiences clearly and unambiguously. Learning skills. On completion, the student will be able to continue to study in a manner that may be largely selfdirected or autonomous.
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CAVALIERI Salvatore
(programma)
The aim of the course is to provide an in-depth introduction to i) the basic principles of the smart manufacturing; ii) basic principles on management problems of a production plants, with particular enphasis on failure management, predictive maintenance; iii) the most important methodologies and techniques used in industries to realise the “Failure Prediction and Predictive Maintenance”.
Smart Manufacturing. Main problems in the management of a production plant. Data Science applications to smart manufacturing. Basic principles on failure detection and predictive maintenance. Failure Detection and Predictive Maintenance. Data Acquisition. Data Processing: signal processing, feature extraction, and feature selection. Data labelling techniques. Machine learning techniques for failure type detection and predictive maintenance. Performance Evaluation. Real case studies using R language. Real case studies using Microsoft Azure Machine Learning Platform. [1] - Patrick Jahnke, "Machine Learning Approaches for Failure Type Detection and Predictive Maintenance", Master Thesis, June 19, 2015. Available online.
[2] - Handouts available in the web site of the course (Studium platform https://studium.unict.it). [3] - V.Fontama, R.Barga, H.Tok, "Predictive Analytics with Microsoft Azure Machine Learning", Apress. |
6 | ING-INF/05 | 40 | - | - | - | - | Attività formative caratterizzanti | ENG | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Secondo semestre
| Insegnamento | CFU | SSD | Ore Lezione | Ore Eserc. | Ore Lab | Ore Altro | Ore Studio | Attività | Lingua | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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9793877 -
ADVANCED MACHINE LEARNING AND KNOWLEDGE DISCOVERY
(obiettivi)
ADVANCED MACHINE LEARNING
The module will focus on the implementations of various machine learning techniques and their applications in various domains. The primary tools used in the class are the Python programming language and several associated libraries. KNOWLEDGE DISCOVERY This module covers the fundamental concepts of deep learning methods and how to use them for extracting, modelling and visualizing the learned knowledge. Topics include: neural networks with backpropagation, convolutional neural networks, recurrent neural networks, methods for representation learning, and how to use them under different learning regimes (supervised, unsupervised and reinforcement learning) and in variety of real-world applications ranging from computer vision, machine translation and medical image analysis. The learning objectives are: to understand and use the main methodologies and techniques for learning from data to understand the main methodologies to design and implement neural networks for real-world applications to understand how to extract and learn knowledge in scenarios when supervision cannot be provided to understand and foresee the reliability of machine learning methods in operational scenarios. Knowledge and understanding To understand the main concepts of learning from data To understand concepts and tools for building intelligent systems using supervision and no supervision To understand the most important machine learning and artificial intelligence methodologies and techniques used by industries to make sense of data in order to support the decision process To understand what are the most appropriate techniques to be used in different real-world applications Applying knowledge and understanding To be able to effectively understand and use the main tools for creating, loading and manipulating datasets. To design and implement from scratch a machine learning system following application-derived constraints in terms of modelling and data To understand proper benchmarks and baselines and analysing achieved results and their generalization in real-world applications To be able to apply methodologies and techniques to analyse data. |
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| - ADVANCED MACHINE LEARNING | Erogato anche in altro semestre o anno | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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KNOWLEDGE DISCOVERY
(obiettivi)
ADVANCED MACHINE LEARNING
The module will focus on the implementations of various machine learning techniques and their applications in various domains. The primary tools used in the class are the Python programming language and several associated libraries. KNOWLEDGE DISCOVERY This module covers the fundamental concepts of deep learning methods and how to use them for extracting, modelling and visualizing the learned knowledge. Topics include: neural networks with backpropagation, convolutional neural networks, recurrent neural networks, methods for representation learning, and how to use them under different learning regimes (supervised, unsupervised and reinforcement learning) and in variety of real-world applications ranging from computer vision, machine translation and medical image analysis. The learning objectives are: to understand and use the main methodologies and techniques for learning from data to understand the main methodologies to design and implement neural networks for real-world applications to understand how to extract and learn knowledge in scenarios when supervision cannot be provided to understand and foresee the reliability of machine learning methods in operational scenarios. Knowledge and understanding To understand the main concepts of learning from data To understand concepts and tools for building intelligent systems using supervision and no supervision To understand the most important machine learning and artificial intelligence methodologies and techniques used by industries to make sense of data in order to support the decision process To understand what are the most appropriate techniques to be used in different real-world applications Applying knowledge and understanding To be able to effectively understand and use the main tools for creating, loading and manipulating datasets. To design and implement from scratch a machine learning system following application-derived constraints in terms of modelling and data To understand proper benchmarks and baselines and analysing achieved results and their generalization in real-world applications To be able to apply methodologies and techniques to analyse data. |
6 | ING-INF/05 | 40 | - | - | - | - | Attività formative caratterizzanti | ENG | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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9793963 -
ELECTIVE COURSE
(obiettivi)
...................................
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12 | 80 | - | - | - | - | Attività formative a scelta dello studente (art.10, comma 5, lettera a) | ENG | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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9795513 -
Final dissertation
(obiettivi)
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Research for and writing of the final dissertation
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10 | - | - | - | 250 | - | Per la prova finale e la lingua straniera (art.10, comma 5, lettera c) | ITA | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Presentation and discussion of the final dissertation
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2 | - | - | - | 50 | - | Per la prova finale e la lingua straniera (art.10, comma 5, lettera c) | ITA | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Business and economics data scientist
Primo anno
Primo semestre
| Insegnamento | CFU | SSD | Ore Lezione | Ore Eserc. | Ore Lab | Ore Altro | Ore Studio | Attività | Lingua | ||||||||||||||||||||||||||||
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9793926 -
DATA BASE AND BIG DATA ANALYTICS
(obiettivi)
The course covers the fundamental concepts of management and design of database systems.
Topics include data models (relational); query languages (SQL); implementation techniques of database management systems (index structures and query processing); and noSQL databases. The learning objectives are: a) To understand and use the main technologies for database management; b) To design a relational database (and not), from a conceptual, logical and physical perspective; c) To use SQL language for performing efficient queries in cases of large datasets; and d) To create and query large scale datasets. |
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DATA BASE
(obiettivi)
The course covers the fundamental concepts of management and design of database systems.
Topics include data models (relational); query languages (SQL); implementation techniques of database management systems (index structures and query processing); and noSQL databases. The learning objectives are: a) To understand and use the main technologies for database management; b) To design a relational database (and not), from a conceptual, logical and physical perspective; c) To use SQL language for performing efficient queries in cases of large datasets; and d) To create and query large scale datasets. |
6 | ING-INF/05 | 40 | - | - | - | - | Attività formative caratterizzanti | ENG | ||||||||||||||||||||||||||||
| - BIG DATA ANALYTICS | Erogato anche in altro semestre o anno | ||||||||||||||||||||||||||||||||||||
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9793875 -
DATA ANALYSIS AND STATISTICAL LEARNING
(obiettivi)
1. Knowledge and understanding (Conoscenza e capacità di comprensione). The first “Statistical Learning” module mainly concerns the fundamentals of two of the main methods used in unsupervised learning: principal component analysis and cluster analysis.
2. Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, the student will be able: i) to implement the main methods used in unsupervised learning; ii) to summarize the main features of a dataset and extract knowledge from data properly. 3. Making judgements (Autonomia di giudizio). On completion, the student will be able to choose a suitable statistical model, apply it, and perform the analysis using a statistical software. 4. Communication skills (Abilità comunicative). On completion, the student will be able to present the results from the statistical analysis, and which conclusions can be drawn. 5. Learning skills (Capacità di apprendimento). On completion, the student will be able to understand the structure of unsupervised learning. |
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DATA ANALYSIS
(obiettivi)
DATA ANALYSIS
1. Knowledge and understanding (Conoscenza e capacità di comprensione). The first “Statistical Learning” module mainly concerns the fundamentals of two of the main methods used in unsupervised learning: principal component analysis and cluster analysis. 2. Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, the student will be able: i) to implement the main methods used in unsupervised learning; ii) to summarize the main features of a dataset and extract knowledge from data properly. 3. Making judgements (Autonomia di giudizio). On completion, the student will be able to choose a suitable statistical model, apply it, and perform the analysis using a statistical software. 4. Communication skills (Abilità comunicative). On completion, the student will be able to present the results from the statistical analysis, and which conclusions can be drawn. 5. Learning skills (Capacità di apprendimento). On completion, the student will be able to understand the structure of unsupervised learning. STATISTICAL LEARNING Knowledge and understanding (Conoscenza e capacità di comprensione). The objectives of the module aim at acquiring knowledge about: i) setting of the learning problem and introducing the general model of the risk functional from empirical data; ii) main statistical learning techniques for regression and data classification. Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, The student will be able: i) to implement main statistical models for supervised and unsupervised learning; ii) to summarize the main features of a dataset and extract knowledge from data properly. Making judgements (Autonomia di giudizio). On completion, students will able how to choose a suitable statistical model, apply sound statistical methods, and perform the analyses using statistical software Communication skills (Abilità comunicative). On completion, students will be able how to present the results from the statistical analyses, and which conclusions can be drawn from the analyses. Learning skills (Capacità di apprendimento). On completion, students will be able to understand the structure of the statistical learning. |
6 | SECS-S/01 | 40 | - | - | - | - | Attività formative caratterizzanti | ENG | ||||||||||||||||||||||||||||
| - STATISTICAL LEARNING | Erogato anche in altro semestre o anno | ||||||||||||||||||||||||||||||||||||
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9793879 -
OPTIMIZATION
(obiettivi)
This graduated-level course introduces analytic tools and optimization methods that are suitable for large-scale problems arising in data science applications. The course presents both basic and advanced concepts of optimization and explores several algorithms that are efficient for network problems.
The student will acquire the ability to formulate, in mathematical terms, problems related to profit maximization and cost minimization, optimization of resources, and traffic network equilibria. The goals of the course are: Knowledge and understanding: the aim of the course is to acquire advanced knowledge that allows students to study optimization problems and model techniques of large-scale decision-making problems. The students will be able to use algorithms for both linear and nonlinear programming problems. Applying knowledge and understanding: students will acquire knowledge useful to identify and model real-life decision-making problems. In addition, through real examples, the student will be able to implement correct solutions for complex problems. Making judgments: students will be able to choose and solve autonomously complex decision-making problems and to interpret the solutions. Communication skills: students will acquire base communication and reading skills using technical language. Learning skills: the course provides students with theoretical and practical methodologies and skills to deal with large-scale optimization problems.
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RACITI Fabio
(programma)
Linear Programming (LP)(about 13 h)
LP models; Graphical method; Simplex method; Duality; Sensitivity analysis Integer Linear Programming (ILP)(about 9 h) Branch & Bound method; 0-1 programming; Knapsack problem Software(about 5 hours) Matlab, Mathematica, Network problems(about 13 h) Graphs (Kruskal, Dijkstra, Bellman-Kalaba algorithms) J. Stacho, Introduction to Operations Research, Columbia University, NY,http://www.cs.toronto.edu/~stacho/public/IEOR4004-notes1.pdf M.S. Bazaraa, J.J. Jarvis, H.D. Sherali, Linear Programming and Network Flows, John Wiley & Sons, 2009. F. Hillier, G.J. Liebermann, “Introduction to Operations Research”, McGraw-Hill, 2006 |
6 | MAT/09 | 40 | - | - | - | - | Attività formative caratterizzanti | ENG | ||||||||||||||||||||||||||||
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9793940 -
STATISTICAL LABORATORY
(obiettivi)
1.The objectives aim at introducing the knowledge of the R language for statistical data analysis with special focus on descriptive statistics, probability distributions and statistical inference.
2.On completion. Students will be able to utilize the R language for: i) providing basic statistical analyses of data; ii) simulating data according to given probability distributions; iii) applying main methods of statistical inference. 3.On completion, students will able to extract knowledge from data through statistical analyses in R. 4.On completion, students will be able how to present the results from the statistical analyses, based on the use of the statistical software R. 5.On completion, students will able how to utilize the statistical software R for basic data analysis and modeling.
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ORTIS ALESSANDRO
(programma)
Use of the statistical software in R regarding:
Descriptive Statistics. Simple Statistical Distributions. Data tables. Frequency distributions. Main summary statistics: arithmetic mean, geometric mean, harmonic mean. Median and percentiles. Variance, standard deviation, relative variation. Graphical representations. Multiple Statistical Distributions. Contingency Tables. Joint distributions, marginal and conditional distributions. Covariance and correlation. Probability.Random number generation and data modeling according to different probability distributions: uniform, binomial, Poisson, Gaussian. Statistical inference.Sample distributions: Student-t, chi-square. Confidence estimation. Confidence level. Confidence bounds for means, variances, proportions. Hypothesis testing. Null hypotheses and alternative hypotheses. P-values. Statistical tests for means, variances, proportions, comparison of means, comparison of proportions. Statistical models.The simple regression model. Goodness of fit. Residual analysis. Inference on the parameters of a linear regression model. Documents available on the web page of The R Project for Statistical Computing: https://www.r-project.org and other resources available on the web.
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3 | - | - | 36 | - | - | Ulteriori attività formative (art.10, comma 5, lettera d) | ENG | |||||||||||||||||||||||||||||
Secondo semestre
| Insegnamento | CFU | SSD | Ore Lezione | Ore Eserc. | Ore Lab | Ore Altro | Ore Studio | Attività | Lingua |
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9793926 -
DATA BASE AND BIG DATA ANALYTICS
(obiettivi)
The course covers the fundamental concepts of management and design of database systems.
Topics include data models (relational); query languages (SQL); implementation techniques of database management systems (index structures and query processing); and noSQL databases. The learning objectives are: a) To understand and use the main technologies for database management; b) To design a relational database (and not), from a conceptual, logical and physical perspective; c) To use SQL language for performing efficient queries in cases of large datasets; and d) To create and query large scale datasets. |
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| - DATA BASE | Erogato anche in altro semestre o anno | ||||||||
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BIG DATA ANALYTICS
(obiettivi)
he course covers the fundamental concepts of management and design of database systems.
Topics include data models (relational); query languages (SQL); implementation techniques of database management systems (index structures and query processing); and noSQL databases. The learning objectives are: a) To understand and use the main technologies for database management; b) To design a relational database (and not), from a conceptual, logical and physical perspective; c) To use SQL language for performing efficient queries in cases of large datasets; and d) To create and query large scale datasets. |
6 | ING-INF/05 | 40 | - | - | - | - | Attività formative caratterizzanti | ENG |
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9793875 -
DATA ANALYSIS AND STATISTICAL LEARNING
(obiettivi)
1. Knowledge and understanding (Conoscenza e capacità di comprensione). The first “Statistical Learning” module mainly concerns the fundamentals of two of the main methods used in unsupervised learning: principal component analysis and cluster analysis.
2. Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, the student will be able: i) to implement the main methods used in unsupervised learning; ii) to summarize the main features of a dataset and extract knowledge from data properly. 3. Making judgements (Autonomia di giudizio). On completion, the student will be able to choose a suitable statistical model, apply it, and perform the analysis using a statistical software. 4. Communication skills (Abilità comunicative). On completion, the student will be able to present the results from the statistical analysis, and which conclusions can be drawn. 5. Learning skills (Capacità di apprendimento). On completion, the student will be able to understand the structure of unsupervised learning. |
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| - DATA ANALYSIS | Erogato anche in altro semestre o anno | ||||||||
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STATISTICAL LEARNING
(obiettivi)
DATA ANALYSIS
1. Knowledge and understanding (Conoscenza e capacità di comprensione). The first “Statistical Learning” module mainly concerns the fundamentals of two of the main methods used in unsupervised learning: principal component analysis and cluster analysis. 2. Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, the student will be able: i) to implement the main methods used in unsupervised learning; ii) to summarize the main features of a dataset and extract knowledge from data properly. 3. Making judgements (Autonomia di giudizio). On completion, the student will be able to choose a suitable statistical model, apply it, and perform the analysis using a statistical software. 4. Communication skills (Abilità comunicative). On completion, the student will be able to present the results from the statistical analysis, and which conclusions can be drawn. 5. Learning skills (Capacità di apprendimento). On completion, the student will be able to understand the structure of unsupervised learning. STATISTICAL LEARNING Knowledge and understanding (Conoscenza e capacità di comprensione). The objectives of the module aim at acquiring knowledge about: i) setting of the learning problem and introducing the general model of the risk functional from empirical data; ii) main statistical learning techniques for regression and data classification. Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). On completion, The student will be able: i) to implement main statistical models for supervised and unsupervised learning; ii) to summarize the main features of a dataset and extract knowledge from data properly. Making judgements (Autonomia di giudizio). On completion, students will able how to choose a suitable statistical model, apply sound statistical methods, and perform the analyses using statistical software Communication skills (Abilità comunicative). On completion, students will be able how to present the results from the statistical analyses, and which conclusions can be drawn from the analyses. Learning skills (Capacità di apprendimento). On completion, students will be able to understand the structure of the statistical learning. |
6 | SECS-S/01 | 40 | - | - | - | - | Attività formative caratterizzanti | ENG |
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9793876 -
DIGITAL INNOVATION AND TRANSFORMATION MANAGEMENT
(obiettivi)
Digital Innovation and Transformation Management provides a comprehensive suite of strategy concepts, tools, methods and perspectives to understand and manage your way through a digital transformation and to develop a strategic response to the emerging digital revolution and to then align your organization for effective strategy execution.
Digital Innovation and Transformation Management course is designed to lead and execute digital innovation initiatives and develop new business models for existing and nascent companies in a wide range of industries. Future-proof your organization by leveraging human-centred innovation and the power of digital technologies, to tap into customer insights and to deliver sustained competitive advantage by developing new products and services, entrepreneurial initiatives, innovative start-ups, consulting in strategic, marketing and R&D management. This course serves as the capstone course for the Data science for management program. As a capstone, the goal is to integrate technological and managerial perspectives. The course provides a comprehensive suite of strategy concepts, tools, methods and perspectives to understand and manage your way through a digital transformation and to develop a strategic response to the emerging digital revolution and to then align your organization for effective strategy execution. The course is designed to lead and execute digital innovation initiatives and develop new business models for existing and nascent companies in entrepreneurial ecosystem. Students will be able to learn the key theoretical and conceptual categories (knowledge and understanding) that show an entrepreneurial approach to digital innovation and transformation management. Students will be able to apply professional scheme, models and tools learned using cases studies, also making judgements and contributing to lecture interactions during presentations and discussions in class (communication skills). Students will be able to learn how to learn, since digital innovation generates changes and evolves over time.
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ROMANO Marco
(programma)
DETAILED COURSE CONTENT
The course focuses on economic, strategic and technological dimensions of designing and developing digital solutions and business models to shape and lead technological and social impacts taking place in distributed innovation networks and ecosystems. The managerial perspective emphasis the different organizational role at every level, e.g. project manager, team manager, functional manager, general manager, CEO, Founder, Startupper, that are involved in digital innovation and transformation enhancing the company competitiveness. Currently, organizations with successful transformations deploy more technologies than others developing digital innovation, building managerial capabilities and planning talent development practices in the following critical technological areas of interest of the course: traditional web technologies, cloud-based services, mobile internet technologies, big data and big data architecture (eg, data lakes), internet of things, design thinking, artificial-intelligence tools, robotics (eg, robotic process automation), advanced neural machine-learning techniques (eg, deep learning), augmented-reality technologies, additive manufacturing (eg, 3-D printing). In details the course will help you learn: the business, economic and technological dimensions to manage strategically the digital revolution; the entrepreneurial trade-off between existing business models and new digitally enhanced emphasizing platforms and ecosystems; the competitive collaborations among companies with different digital business models; how to shape and lead innovative solutions (product and service) in the digital contest. An important part of the course are the several happenings and seminars, which will bring together leading practitioners at the cutting edge of digital technologies to discuss emerging issues. TEXTBOOK INFORMATION
Textbook:BRESCIANI S., FERRARIS A., ROMANO M., SANTORO G. (2021). Digital Transformation Management for Agile Organizations. p. 1-216, Howard House, Wagon Lane, Bingley BD16 1WA, UK:Emerald Publishing Limited, ISBN: 9781800431720. Reading Materials will be announced in class and electronically. |
9 | SECS-P/08 | 60 | - | - | - | - | Attività formative caratterizzanti | ENG |
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9793878 -
BEHAVIORAL ECONOMICS AND COMPLEXITY
(obiettivi)
1.The course aims to build consciousness on the roots of complexity in human behaviors, with reference to their consequences on dynamic perspectives of economic relations. Main contributions of related literature will provide the methodological approach to understand socio-economic relations, by comparing theoretical models and empirical data.
2.The course will analyze both the micro- and the macro-economic perspective, by underlining, respectively, the behavioral approach in the individual choice paradigm and the emergent dynamics in collective phenomena. An essential introduction to agent-based modelling will be given, as one of the most adequate tools of analysis in the field. 3.The course will provide students with adequate abilities to distinguish complex phenomena and to reconcile correct modeling structures with socio-economic problems at hands. 4.The course has an experimental nature. It deals with borderline topics and non-standard approaches for economic analysis. Therefore, a specific effort will be done to help students learning the appropriate terminology and the ability to discuss actual aspects of studied concepts. 5.The course will be a starting point more than a consolidated set of results. All teaching materials, references and presented topics will create a toolbox for many possible future developments, for both further studies and professional applications.
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BIONDO ALESSIO EMANUELE
(programma)
The course aims to present the developments in the context of Micro- and Macroeconomic Theory deriving from the analysis of, respectively, the behavioral approach in the choice paradigm and the emergent dynamics in collective phenomena. Thus, the first part of the course will provide a broad introduction to the concept of rationality in economics and to the advances of modern literature related to the implementation of bounded rationality and behavioral approaches. The second part, instead, will be focused on the concepts of complexity, heterogeneity and interaction in economic models, and special attention will be devoted to the study of macroeconomic agent-based models. For both blocks, a step-by-step introduction of essential ABM building elements will be provided. Finally, a user-friendly open access platform will be presented in order to enable students to build their own models and perform simulations to be confronted with empirical data for policies assessment.
A - Economic Behavior and Microeconomics: Rational choice in Economics and Bounded Rationality; Introduction to Computational Economics. B - Complexity and Macroeconomics: Complexity and Emergent Macroeconomics; Network Economics, Agent-based Modeling. Becker G.S.,The Economic Approach to Human Behavior, The University of Chicago Press, 1976, chapters: 1, 7, 8, 12, 13.
Gilboa I.,Rational Choice, 2010, The MIT Press, Cambridge. Simon H.A., (1955) A Behavioral Model of Rational Choice,The Quarterly Journal of Economics, vol.69, No.1., pp.99-118. Kahneman D., Tversky A., (1979)An Analysis of Decision under Risk,Econometrica, Vol. 47, No. 2 (Mar., 1979), pp. 263-291 Kahneman D., Tversky A., (1984)Choice, Values, Frames,American Psychologist,vol. 39, No. 4, 341-350. Cioffi-RevillaC.,Introduction to Computational Social Science, Springer 2014, chapters: 5, 6, 7. Delli Gatti D., Gaffeo E., Gallegati M., Giulioni G., Palestrini A.,Emergent Macroeconomics, 2008, Springer-Verlag, Milan. Jackson M.O.,Social and Economic Networks, 2010, Princeton University Press, chapters: 2, 3, 6. |
9 | SECS-P/02 | 60 | - | - | - | - | Attività formative caratterizzanti | ENG |
Secondo anno
Primo semestre
| Insegnamento | CFU | SSD | Ore Lezione | Ore Eserc. | Ore Lab | Ore Altro | Ore Studio | Attività | Lingua | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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9793877 -
ADVANCED MACHINE LEARNING AND KNOWLEDGE DISCOVERY
(obiettivi)
ADVANCED MACHINE LEARNING
The module will focus on the implementations of various machine learning techniques and their applications in various domains. The primary tools used in the class are the Python programming language and several associated libraries. KNOWLEDGE DISCOVERY This module covers the fundamental concepts of deep learning methods and how to use them for extracting, modelling and visualizing the learned knowledge. Topics include: neural networks with backpropagation, convolutional neural networks, recurrent neural networks, methods for representation learning, and how to use them under different learning regimes (supervised, unsupervised and reinforcement learning) and in variety of real-world applications ranging from computer vision, machine translation and medical image analysis. The learning objectives are: to understand and use the main methodologies and techniques for learning from data to understand the main methodologies to design and implement neural networks for real-world applications to understand how to extract and learn knowledge in scenarios when supervision cannot be provided to understand and foresee the reliability of machine learning methods in operational scenarios. Knowledge and understanding To understand the main concepts of learning from data To understand concepts and tools for building intelligent systems using supervision and no supervision To understand the most important machine learning and artificial intelligence methodologies and techniques used by industries to make sense of data in order to support the decision process To understand what are the most appropriate techniques to be used in different real-world applications Applying knowledge and understanding To be able to effectively understand and use the main tools for creating, loading and manipulating datasets. To design and implement from scratch a machine learning system following application-derived constraints in terms of modelling and data To understand proper benchmarks and baselines and analysing achieved results and their generalization in real-world applications To be able to apply methodologies and techniques to analyse data. |
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ADVANCED MACHINE LEARNING
(obiettivi)
ADVANCED MACHINE LEARNING
The module will focus on the implementations of various machine learning techniques and their applications in various domains. The primary tools used in the class are the Python programming language and several associated libraries. KNOWLEDGE DISCOVERY This module covers the fundamental concepts of deep learning methods and how to use them for extracting, modelling and visualizing the learned knowledge. Topics include: neural networks with backpropagation, convolutional neural networks, recurrent neural networks, methods for representation learning, and how to use them under different learning regimes (supervised, unsupervised and reinforcement learning) and in variety of real-world applications ranging from computer vision, machine translation and medical image analysis. The learning objectives are: to understand and use the main methodologies and techniques for learning from data to understand the main methodologies to design and implement neural networks for real-world applications to understand how to extract and learn knowledge in scenarios when supervision cannot be provided to understand and foresee the reliability of machine learning methods in operational scenarios. Knowledge and understanding To understand the main concepts of learning from data To understand concepts and tools for building intelligent systems using supervision and no supervision To understand the most important machine learning and artificial intelligence methodologies and techniques used by industries to make sense of data in order to support the decision process To understand what are the most appropriate techniques to be used in different real-world applications Applying knowledge and understanding To be able to effectively understand and use the main tools for creating, loading and manipulating datasets. To design and implement from scratch a machine learning system following application-derived constraints in terms of modelling and data To understand proper benchmarks and baselines and analysing achieved results and their generalization in real-world applications To be able to apply methodologies and techniques to analyse data. |
6 | INF/01 | 40 | - | - | - | - | Attività formative caratterizzanti | ENG | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| - KNOWLEDGE DISCOVERY | Erogato anche in altro semestre o anno | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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9793884 -
HIGH TECH MARKETS, INDUSTRIAL ORGANIZATION AND GROWTH
(obiettivi)
Knowledge and understanding (Conoscenza e capacità di comprensione). The unit aims to provide knowledge of the main economic aspects related to Information & Communication Technologies (ICT) along with both their link to some Industrial Organisation (IO) issues and their implications for economic growth.
Applying knowledge and understanding (Capacità di applicare conoscenza e comprensione). The unit aims to develop skills in applying information, using appropriate methods, concepts and theories regarding the ITC, IO, and growth to the analysis of real-world cases. Making judgments (Autonomia di giudizio). Successful students will be able to select a proper economic model in order to analyse both theoretical and real-world cases. Communication skills (Abilità comunicative). Successful students will be familiar with both the terms and the narrative related to High Technology, Industrial Organisation and Growth. Furthermore, they will be able to communicate to a variety of audiences including experts, practitioners, and the general public. Learning skills (Capacità di apprendimento). Successful students will be able to understand which theoretical concept is appropriate to deal with specific problems in the field of high technology, industrial organisation, and its link to growth.
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TORRISI GIANPIERO
(programma)
Industrial Organisation of Hi-Tech Markets.The economics of Information and Communication Technologies (ICT): an introduction. ICT investments in Research and Development (R&D). Small firms grow online. ICT as general-purpose technologies. ICT and information goods. ICT as a product. Facebook: network effects in action.There’s an app for that:a mention to apps pricing issues and strategies.Google: the new Big Brother?ICT, the economic value of information, and related privacy issues.
Digital Markets.Market efficiency. Empirical evidence on digital markets efficiency. Price dispersion on digital markets. Versioning and building. Dynamic pricing. Network Externalities.Network externalities and critical mass. The dynamics of technology adoption. Competition, compatibility and standardisation. Strategies towards standardisation. Switching costs and network externalities. Access and Interconnection in Telecommunications. One-way access. Access pricing with imperfect downstream competition. Local loop unbundling. Access and investments: the ladder of investments theory. Two-way access and interconnection. Interconnection fee and collusion. Interconnection and calling rates. Interconnection and the “receving party pays” regime. Cumulative Innovation in Dynamic Industries.Patents and other appropriability mechanism. Standing on the shoulders of giants. Patent thickets and anticommons. The issue of weak patents. High-technology industry and growth.Innovation and Growth: The Schumpeterian Perspective. Cross-country comparative analysis. Recommended textbook
Comino, S., & Manenti, F. M. (2014).Industrial Organisation of High-technology Markets: The Internet and Information Technologies. Edward Elgar Publishing. Further readings Aghion, P., Akcigit, U. 2015. Innovation and growth: the Schumpeterian perspective. Paper presented at the COEURE Coordination Action Workshop, June, 23rd, Université Libre de Bruxelles, Brussels. Benko, G. (2000). Technopoles, high-tech industries and regional development: A critical review.GeoJournal,51(3), 157-167. Jenkins, J. C., Leicht, K. T., & Jaynes, A. (2006). Do High Technology Policies Work? High Technology Industry Employment Growth in US Metropolitan Areas, 1988–1998.Social forces,85(1), 267-296. Keeble, D. E. (1989). High-technology industry and regional development in Britain: the case of the Cambridge phenomenon.Environment and Planning C: Government and Policy,7(2), 153-172. Lucchese, M., Nascia, L., & Pianta, M. (2016). Industrial policy and technology in Italy.Economia e politica industriale,43(3), 233-260. Stam, E., De Jong, J. P., & Marlet, G. (2008). Creative industries in the Netherlands: Structure, development, innovativeness and effects on urban growth.Geografiska Annaler: series B, human geography,90(2), 119-132. Tong, J. (2005). High‐Tech And High Capability In A Growth Model.International Economic Review,46(1), 215-243. |
6 | SECS-P/06 | 40 | - | - | - | - | Attività formative caratterizzanti | ENG | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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9793885 -
ACCOUNTING INFORMATION SYSTEMS
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6 | SECS-P/07 | 40 | - | - | - | - | Attività formative caratterizzanti | ENG | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Secondo semestre
| Insegnamento | CFU | SSD | Ore Lezione | Ore Eserc. | Ore Lab | Ore Altro | Ore Studio | Attività | Lingua | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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9793877 -
ADVANCED MACHINE LEARNING AND KNOWLEDGE DISCOVERY
(obiettivi)
ADVANCED MACHINE LEARNING
The module will focus on the implementations of various machine learning techniques and their applications in various domains. The primary tools used in the class are the Python programming language and several associated libraries. KNOWLEDGE DISCOVERY This module covers the fundamental concepts of deep learning methods and how to use them for extracting, modelling and visualizing the learned knowledge. Topics include: neural networks with backpropagation, convolutional neural networks, recurrent neural networks, methods for representation learning, and how to use them under different learning regimes (supervised, unsupervised and reinforcement learning) and in variety of real-world applications ranging from computer vision, machine translation and medical image analysis. The learning objectives are: to understand and use the main methodologies and techniques for learning from data to understand the main methodologies to design and implement neural networks for real-world applications to understand how to extract and learn knowledge in scenarios when supervision cannot be provided to understand and foresee the reliability of machine learning methods in operational scenarios. Knowledge and understanding To understand the main concepts of learning from data To understand concepts and tools for building intelligent systems using supervision and no supervision To understand the most important machine learning and artificial intelligence methodologies and techniques used by industries to make sense of data in order to support the decision process To understand what are the most appropriate techniques to be used in different real-world applications Applying knowledge and understanding To be able to effectively understand and use the main tools for creating, loading and manipulating datasets. To design and implement from scratch a machine learning system following application-derived constraints in terms of modelling and data To understand proper benchmarks and baselines and analysing achieved results and their generalization in real-world applications To be able to apply methodologies and techniques to analyse data. |
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| - ADVANCED MACHINE LEARNING | Erogato anche in altro semestre o anno | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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KNOWLEDGE DISCOVERY
(obiettivi)
ADVANCED MACHINE LEARNING
The module will focus on the implementations of various machine learning techniques and their applications in various domains. The primary tools used in the class are the Python programming language and several associated libraries. KNOWLEDGE DISCOVERY This module covers the fundamental concepts of deep learning methods and how to use them for extracting, modelling and visualizing the learned knowledge. Topics include: neural networks with backpropagation, convolutional neural networks, recurrent neural networks, methods for representation learning, and how to use them under different learning regimes (supervised, unsupervised and reinforcement learning) and in variety of real-world applications ranging from computer vision, machine translation and medical image analysis. The learning objectives are: to understand and use the main methodologies and techniques for learning from data to understand the main methodologies to design and implement neural networks for real-world applications to understand how to extract and learn knowledge in scenarios when supervision cannot be provided to understand and foresee the reliability of machine learning methods in operational scenarios. Knowledge and understanding To understand the main concepts of learning from data To understand concepts and tools for building intelligent systems using supervision and no supervision To understand the most important machine learning and artificial intelligence methodologies and techniques used by industries to make sense of data in order to support the decision process To understand what are the most appropriate techniques to be used in different real-world applications Applying knowledge and understanding To be able to effectively understand and use the main tools for creating, loading and manipulating datasets. To design and implement from scratch a machine learning system following application-derived constraints in terms of modelling and data To understand proper benchmarks and baselines and analysing achieved results and their generalization in real-world applications To be able to apply methodologies and techniques to analyse data. |
6 | ING-INF/05 | 40 | - | - | - | - | Attività formative caratterizzanti | ENG | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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9793963 -
ELECTIVE COURSE
(obiettivi)
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12 | 80 | - | - | - | - | Attività formative a scelta dello studente (art.10, comma 5, lettera a) | ENG | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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9795513 -
Final dissertation
(obiettivi)
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Research for and writing of the final dissertation
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10 | - | - | - | 250 | - | Per la prova finale e la lingua straniera (art.10, comma 5, lettera c) | ITA | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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Presentation and discussion of the final dissertation
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2 | - | - | - | 50 | - | Per la prova finale e la lingua straniera (art.10, comma 5, lettera c) | ITA | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
