Bachelor of Science SUPSI in Data Science and Artificial Intelligence

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Dipartimento tecnologie innovative

Bachelor of Science SUPSI in Data
Science and Artificial Intelligence
Piano di studio

Tempo pieno
Anno accademico 2021/2022

Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2021/2022   1 di 43
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Calculus 1
Codice                           M-B1010E.1

Crediti                          9.0 ECTS                          Semestre di riferimento            1°

Durata                           2 semestri                        Tipo di modulo                     Obbligatorio

Responsabile modulo              Rezzonico Rossetti Paola

Corsi
                                                                                                           Ore SA    Ore SP
C-B1011E.1                       Calculus 1                                                                    4.0       4.0
E-B1011E.1                       Ex. calculus 1                                                                2.0       2.0

                                                                                      Totale ore settimanali: 6.0        6.0

Descrittivo dei corsi
Calculus 1
Codice                           C-B1011E.1

Obiettivi                        Stabilization of basic mathematical concepts
                                 Improvement of written and oral mathematical communication skills
                                 Capacity of using proper tools for numerical calculus
                                 Knowledge of main differential and integral calculus concepts
                                 Capacity of applying main differential and integral calculus concepts

Contenuti                        Critical thinking
                                 Review of real functions and equations
                                 Limits of functions
                                 Differential calculus and applications
                                 Integral calculus and applications
                                 Taylor polynomials
                                 Planar curves
                                 Complex numbers
                                 Lab in Python

Metodo di                        Interactive lessons with exercises.
insegnamento

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Linear algebra 1
Codice                           M-B1020E.1

Crediti                          6.0 ECTS                          Semestre di riferimento            1°

Durata                           2 semestri                        Tipo di modulo                     Obbligatorio

Responsabile modulo              Fontana Martina

Corsi
                                                                                                           Ore SA    Ore SP
C-B1021E.1                       Linear Algebra                                                                2.0       2.0
E-B1021E.1                       Ex. Linear Algebra                                                            2.0       2.0

                                                                                      Totale ore settimanali: 4.0        4.0

Descrittivo dei corsi
Linear Algebra
Codice                           C-B1021E.1

Obiettivi                        Development scientific reasoning by means of abstraction
                                 Knowledge of matrix computation
                                 Ability of manipulating matrices
                                 Capacity of using proper tools for algebraic calculus, matrix manipulation and
                                 decomposition

Contenuti                        Critical thinking
                                 Vectorial calculus
                                 Scalar, vectorial and mixed product
                                 Analytical geometry
                                 Matrices
                                 Linear applications and geometrical transformations
                                 Determinant
                                 Lab in Python

Metodo di                        Interactive lessons with exercises.
insegnamento

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Probability and Statistics
Codice                           M-B1100E.1

Crediti                          6.0 ECTS                          Semestre di riferimento            1°

Durata                           2 semestri                        Tipo di modulo                     Obbligatorio

Responsabile modulo              Corani Giorgio

Corsi
                                                                                                           Ore SA    Ore SP
C-B1101E.1                       Probability and Statistics                                                    2.0       2.0
E-B1101E.1                       Ex. probability and Statistics                                                2.0       2.0

                                                                                      Totale ore settimanali: 4.0        4.0

Descrittivo dei corsi
Probability and Statistics
Codice                           C-B1101E.1

Obiettivi                        Knowledge of fundamentals notions of probability
                                 Knowldge of main probability distributions
                                 Capability of modelling simple probabilistic problems
                                 Knowledge of fundamentals methods for statistical inference
                                 Basic usage of R
                                 Ability of performing statistical tests and doing inference
                                 Ability of performing linear regression analysis
                                 Ability of evaluating linear regression performance

Contenuti                        Combinatorics and set theory
                                 Measure theory and probability measure
                                 Random variables and random vectors
                                 Conditional probability and indipendence
                                 Bayes rule
                                 Univariate and multivariate distributions
                                 Descriptive statistics, position index dispertion index
                                 Exploratory data analysis
                                 Statistical inference:
                                 Maximum likelihood estimation)
                                 Tests and confidence intervals
                                 Simple linear regression and regression metrics (MAE, MSE, RMSE, Rsq)
                                 Overview of Bayesian inference
                                 Lab in R

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Numerical analysis and optimisation
Codice                           M-B1110E.1

Crediti                          6.0 ECTS                          Semestre di riferimento            1°

Durata                           2 semestri                        Tipo di modulo                     Obbligatorio

Responsabile modulo              Barta Janos

Corsi
                                                                                                           Ore SA    Ore SP
C-B1111E.1                       Numerical analysis and optimisation                                           2.0       2.0
E-B1111E.1                       Ex. numerical analysis and optimisation                                       2.0       2.0

                                                                                      Totale ore settimanali: 4.0        4.0

Descrittivo dei corsi
Numerical analysis and optimisation
Codice                           C-B1111E.1

Obiettivi                        Knowledge of main numerical formats
                                 Knowledge of principal numerical methods
                                 Capability of solving mathematical problems by means of algorithms
                                 Capability of implementing numerical algorithms in python/R

Contenuti                        Integer and real number representations
                                 Numerical methods for solving equations
                                 Linear system resolution methods
                                 Interpolation methods
                                 Numerical integration methods
                                 Introduction to optimization methods
                                 Lab in Python/R

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Inglese B2
Codice                           M-C1020.1

Crediti                          6.0 ECTS                          Semestre di riferimento            1°

Durata                           2 semestri                        Tipo di modulo                     Obbligatorio

Prerequisiti                     Conoscenze di inglese a livello B1

Metodo di valutazione            Per la certificazione dei moduli di lingua valgono le specifiche direttive interne del
                                 Centro competenze lingue

                                 Osservazioni
                                 I livelli si riferiscono alle sei scale del Common European Framework del
                                 Consiglio d'Europa ripresi nella versione svizzera di un Portfolio europeo delle
                                 lingue (PEL). La verifica del livello richiesto dal dipartimento puo avvenire anche
                                 tramite equipollenza di determinati certificati internazionali riconosciuti dal Centro
                                 competenze lingue o dal superamento di esami prima dell'inizio del corso

Responsabili modulo              Hardie Jan, Rogers Richard Paul

Corsi
                                                                                                           Ore SA    Ore SP
C-C1021.1                        Inglese B2                                                                    4.0       4.0

                                                                                      Totale ore settimanali: 4.0        4.0

Descrittivo dei corsi
Inglese B2
Codice                           C-C1021.1

Obiettivi                        Acquisire e approfondire le competenze linguistiche che favoriscono
                                 l’inserimento nel mondo professionale in Svizzera e all’estero, con particolare
                                 attenzione all’ambito tecnologico
                                 Approfondire le capacità grammaticali e acquisire il lessico specifico per poter
                                 sviluppare conversazioni in diverse situazioni
                                 Sviluppare e approfondire le quattro competenze linguistiche: ascolto, lettura,
                                 espressione orale (conversazione / esposizione), espressione scritta, al fine di
                                 possedere un controllo della lingua appropriato, accurato e fluente.
                                 L’obiettivo dell’insegnamento della lingua è il raggiungimento di un livello
                                 intermedio avanzato

Contenuti                        Verranno trattati temi, attinenti all’ambito tecnologico, lavorativo, sociale e
                                 culturale

Metodo di                        Insegnamento comunicativo con discussioni, letture, esercitazioni in gruppo,
insegnamento                     presentazioni orali, simulazioni
                                 La partecipazione alle lezioni è obbligatoria

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Communication and presentation
Codice                           M-C1040E.1

Crediti                          2.0 ECTS                          Semestre di riferimento            1°

Durata                           1 semestre                        Tipo di modulo                     Obbligatorio

Responsabile modulo              Carcano Cristina

Corsi
                                                                                                           Ore SA    Ore SP
C-C1041E.1                       Communication and presentation                                                2.0        -

                                                                                      Totale ore settimanali: 2.0          -

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Ethics and Artificial Intelligence
Codice                           M-D1010E.1

Crediti                          4.0 ECTS                          Semestre di riferimento            1°

Durata                           2 semestri                        Tipo di modulo                     Obbligatorio

Responsabile modulo              Facchini Alessandro

Corsi
                                                                                                           Ore SA    Ore SP
C-D1011E.2                       Introduction to Artificial Intelligence                                       2.0         -
C-D1012E.2                       Ethics of Artificial Intelligence                                               -       2.0
E-D1011E.1                       Ex. introduction to artificial intelligence                                   1.0         -
E-D1012E.1                       Ethics of Artificial Intelligence                                               -       1.0

                                                                                      Totale ore settimanali: 3.0        3.0

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Descrittivo dei corsi
Introduction to Artificial Intelligence
Codice                           C-D1011E.2

Obiettivi                        Acquire basic knowledge on the theoretical basis of AI and machine learning,
                                 and know how the theory has been developed.
                                 Understand and explain the concepts, principles, strengths and limitations of the
                                 main kinds of knowledge representation: the logic approach, Bayesian networks
                                 and the connectionist approach.
                                 Understand and apply some main algorithms for uninformed and heuristic
                                 search.
                                 Understand and explain the main differences between the major kinds of
                                 machine learning problems: supervised learning, unsupervised learning and
                                 reinforcement learning.
                                 Understand and apply some simple algorithms for supervised learning.

Contenuti                        Brief history of Artificial Intelligence
                                 What is an intelligent agent?
                                 Symbolic AI
                                 - What is logic?
                                 - Logical agents: the Wumpus world
                                 - From natural language to formal language
                                 - The syntax and semantics of propositional logic: normal forms
                                 - Proof systems: Resolution
                                 Uninformed search
                                 - Formulating a search problem
                                 - Breadth-first search, depth-first search, and some variants
                                 Heuristic search
                                 - Greedy search and A*-search
                                 Reasoning about uncertainty
                                 - Knowledge representation and reasoning about uncertain domains
                                 - Limits of logical agents
                                 - Bayesian networks
                                 Machine learning: a very first glimpse
                                 - Why machine learning?
                                 - Learning a Bayesian network
                                 - Training a perceptron
                                 - What are (feedforward) neural nets? A very first idea

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Ethics of Artificial Intelligence
Codice                           C-D1012E.2

Obiettivi                        Understand the motivations for designing intelligent machines and their
                                 implications.
                                 Understand and explain some possible ethical and societal impacts of AI and
                                 machine learning.

Contenuti                        The course treats of ethical issues surrounding the development of Artificial
                                 Intelligence:
                                 - Can machine acts intelligently? Who is responsible for their actions?
                                 - What are the risks of developing AI? Do we have to give up privacy to
                                 innovation? How can bias in algorithm be prevented?
                                 - Machines with moral status: can AI be made more ethical?
                                 - Can and should robots have rights?
                                 - Can and should AI be regulated?

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Operating environments
Codice                           M-I1030E.1

Crediti                          4.0 ECTS                          Semestre di riferimento            1°

Durata                           2 semestri                        Tipo di modulo                     Obbligatorio

Responsabile modulo              Mastropietro Roberto

Corsi
                                                                                                           Ore SA    Ore SP
C-I1031E.1                       Operating environments                                                        1.0       1.0
E-I1031E.1                       Ex. operating environments                                                    1.0       1.0

                                                                                      Totale ore settimanali: 2.0        2.0

Descrittivo dei corsi
Operating environments
Codice                           C-I1031E.1

Obiettivi                        Understand the architecture of a computer, the basic components of an operating
                                 system, command-line tools to manage files and to perform processing tasks on
                                 local and remote data.

Contenuti                        Introduction to computer architecture and operating systems
                                 The Bash shell
                                 Commands for directory and file management
                                 Pipes and redirections
                                 Regular expressions
                                 Tools for software management (apt, pip, python distributions, etc.)
                                 Operating system tools for data processing

Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2021/2022                                  11 di 43
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Introduction to Computer programming
Codice                           M-I1070E.1

Crediti                          9.0 ECTS                          Semestre di riferimento            1°

Durata                           2 semestri                        Tipo di modulo                     Obbligatorio

Responsabile modulo              Pedrazzini Sandro

Corsi
                                                                                                           Ore SA    Ore SP
C-I1071E.1                       Introduction to Computer Programming                                          2.0       2.0
E-I1071E.1                       Ex. introduction to computer programming                                      4.0       4.0

                                                                                      Totale ore settimanali: 6.0        6.0

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Descrittivo dei corsi
Introduction to Computer Programming
Codice                           C-I1071E.1

Ore totali                       4.0

Obiettivi                        Acquire essential knowledge on the theoretical basis of computer science
                                 Analyze a problem and translate it into a computer program
                                 Be able of writing simple algorithms ("in the small") solving a well defined
                                 problem using a dynamic language
                                 Learn the fundamental data structures at the basis of all other complex data
                                 structures.
                                 Acquire the knowledge of the most basic algorithms operating on those data
                                 structures.
                                 Basic knowledge of algorithm stability
                                 Uses of classes as data structures

Contenuti                        1. Introduction to programming
                                 A simplified architecture model of a sequential computer
                                 Problem aalysis
                                 Variables and Simple Data Types
                                 Lists and Dictionaries
                                 Conditions and iterations
                                 User Input
                                 Functions and parameters
                                 Recursion
                                 Use of classes
                                 Files and Exceptions

                                 2. Introduction to algortihms and data structures
                                 Data structures and abstract data types
                                 Linked list data structure and its implementation
                                 Stacks and queues
                                 Binary search trees
                                 Balanced trees
                                 Associative arrays and dictionaries
                                 Hashing
                                 Sorting algorithms
                                 Non-comparison based sorting algorithms
                                 Counting sort and radix sort
                                 Algorithm stability

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Project management
Codice                           M-C2020E.1

Crediti                          2.0 ECTS                          Semestre di riferimento          2°

Durata                           1 semestre                        Tipo di modulo                   Obbligatorio

Responsabile modulo              Bassi Antonio

Corsi
                                                                                                         Ore SA    Ore SP
C-C2021E.1                       Project management                                                           -        2.0

                                                                                      Totale ore settimanali: -        2.0

Descrittivo dei corsi
Project management
Codice                           C-C2021E.1

Obiettivi                        Understand the project management lexicon to communicate in a correct way
                                 Provide theoretical and practical knowledge for proper and integrated project
                                 management
                                 Define the project plans to plan and manage the project
                                 Define the project life cycle to improve the project strategy
                                 Define the main tools and techniques to manage the project
                                 Define the processes and the related work flow for an efficient project
                                 management
                                 Understand the power of knowledge to improve the results of the organization

Contenuti                        Project introduction to understand the context
                                 Terminology introduction to use a common lexicon
                                 Scope and objectives definition to define requirements, assumptions and
                                 constraints
                                 Definition of the main components of the project
                                 Time management to define project activities, critical path and project schedule
                                 Cost management to define project budget and the project contingency
                                 Control management to protect the plans with the Earned Value Management
                                 System
                                 Risk management

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Data challenge: Annual project in applied
data science
Codice                           M-D2010E.1

Crediti                          4.0 ECTS                          Semestre di riferimento   2°

Durata                           1 semestre                        Tipo di modulo            Obbligatorio

Responsabile modulo              Corani Giorgio

Corsi
                                                                                                  Ore SA    Ore SP
E-D2011E.1                       Ex. Data challenge: Annual project in applied data                    -        2.0
                                 science
                                                                         Totale ore settimanali: -              2.0

Descrittivo dei corsi
Ex. Data challenge: Annual project in applied data science
Codice                           E-D2011E.1

Obiettivi                        Improve programming skills by working on a real-world data set, performing basic
                                 tasks of data processing, data analysis and data visualization. The activities rely
                                 on the notions learned during the first semester.

Contenuti                        The challenge will propose different tasks, including:
                                 - data acquisition and data formatting
                                 - basic manipulation of data in Python
                                 - simple data visualization using Jupyter notebooks
                                 - implementation of a complete data processing pipeline
                                 - presentation of the results

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Hackathon
Codice                           M-D2020E.1

Crediti                          2.0 ECTS                          Semestre di riferimento          2°

Durata                           1 semestre                        Tipo di modulo                   Obbligatorio

Responsabile modulo              Huber David

Corsi
                                                                                                         Ore SA    Ore SP
E-D2021E.1                       Ex. Hackathon                                                                -        1.0

                                                                                      Totale ore settimanali: -        1.0

Descrittivo dei corsi
Ex. Hackathon
Codice                           E-D2021E.1

Obiettivi                        Practice working as a team on a project anticipating topics that will be covered in
                                 the 2nd semester.

Contenuti                        During the hackathon students will be given a number of high-level goals to be
                                 addressed in teams. The activity will cover both topics that the students already
                                 know from the 1st semester, as well as new topics that will be covered in the
                                 following semester. The hackathon will be loosely guided, so that the students
                                 will have the freedom to explore different solution approaches. The activity will be
                                 closed by an evaluated presentation of the work performed by each team.

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Calculus and Linear algebra 2
Codice                           M-B3010E.1

Crediti                          5.0 ECTS                          Semestre di riferimento            3°

Durata                           1 semestre                        Tipo di modulo                     Obbligatorio

Responsabile modulo              Garzoni Matteo

Corsi
                                                                                                           Ore SA    Ore SP
C-B3011E.1                       Calculus 2                                                                    2.0        -
C-B3012E.1                       Linear algebra 2                                                              2.0        -
E-B3011E.1                       Ex. calculus 2                                                                2.0        -

                                                                                      Totale ore settimanali: 6.0          -

Descrittivo dei corsi
Calculus 2
Codice                           C-B3011E.1

Obiettivi                        Capability of solving ordinary differential equations
                                 Capacity of modelling and solving real/engineering problems with reasoning and
                                 abstraction

Contenuti                        mutivariate functions
                                 applications of integrals
                                 ordinary differential equations

Linear algebra 2
Codice                           C-B3012E.1

Obiettivi                        Knowledge of matrix computation, eigenvalues, eigenvectors and the associated
                                 applications
                                 Ability of manipulating matrices
                                 Capacity of using proper tools for algebraic calculus, matrix manipulation and
                                 decomposition

Contenuti                        eigenvalues and eigenvectors
                                 norms
                                 systems of ODES, linear time-invariant systems
                                 Homogeneous coordinates

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Communication and Reporting
Codice                           M-C3060E.1

Crediti                          2.0 ECTS                          Semestre di riferimento            3°

Durata                           1 semestre                        Tipo di modulo                     Obbligatorio

Responsabile modulo              Carcano Cristina

Corsi
                                                                                                           Ore SA    Ore SP
C-C3061E.1                       Communication and Reporting                                                   2.0        -

                                                                                      Totale ore settimanali: 2.0          -

Descrittivo dei corsi
Communication and Reporting
Codice                           C-C3061E.1

Obiettivi                        Acquire fundamental writing communication skills in order to be able to be:
                                 Complete and short: all information needed by the readers
                                 Correct: true, important, precise, grammatically correct
                                 Credible: supported by arguments

Contenuti                        Basics of written communication:
                                 - strategy (goal and structure contents)
                                 - arguing/informing/motivating/describing texts
                                 - writing formats
                                 - vocabolary and warm words
                                  - bibliography (search and citation)
                                 How to write:
                                  - reports, project reports
                                  - thesis
                                  - abstrat
                                  - poster
                                  - paper
                                  - emails
                                  - meeting minute, memos
                                  - feedback
                                  - social media (Linkedin, post, ..)

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Supervised Learning
Codice                           M-D3010E.1

Crediti                          6.0 ECTS                          Semestre di riferimento            3°

Durata                           1 semestre                        Tipo di modulo                     Obbligatorio

Responsabile modulo              Wand Michael

Corsi
                                                                                                           Ore SA    Ore SP
C-D3011E.1                       Supervised learning                                                           4.0        -
E-D3011E.1                       Ex. Supervised Learning                                                       2.0        -

                                                                                      Totale ore settimanali: 6.0          -

Descrittivo dei corsi
Supervised learning
Codice                           C-D3011E.1

Obiettivi                        Knowledge of the main supervised learning problems (classification, regression)
                                 Intuitive understanding of the inner workings of sophisticated classifiers (trees,
                                 forests, svms)
                                 Understanding of model scoring and comparison metrics
                                 Detailed understanding of the inner workings of simple classification models (nn,
                                 logreg, nbc)
                                 Capability of recognizing underfitting and overfitting symptoms and of properly
                                 addressing
                                 Capability of addressing feature engineering tasks
                                 Capability of selecting and using classification and regression models to solve
                                 practical problems and properly interpreting their results

Contenuti                        Intro to supervised learning and classification
                                 Training-validation-testing split and cross validation strategies
                                 Classification metrics (accuracy, confusion matrix)
                                 Cost-sensitive classification
                                 Model evaluation and ROC
                                 Overfitting and Bias-Variance Tradeoff
                                 Discussion of some Classification models:
                                 - Neural Networks
                                 - K-Nearest Neighbors
                                 - logistic regression
                                 - decision trees
                                 - random forests
                                 - support vector machines (SVM)
                                 Practical problems from key areas
                                 Review of simple linear regression and regression metrics
                                 Multiple Linear Regression
                                 Nonlinear Regression and smoothing
                                 Practical problems from key areas

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Data visualization
Codice                           M-D3020E.1

Crediti                          4.0 ECTS                          Semestre di riferimento            3°

Durata                           1 semestre                        Tipo di modulo                     Obbligatorio

Responsabile modulo              Mazza Riccardo

Corsi
                                                                                                           Ore SA    Ore SP
C-D3021E.1                       Data Visualization                                                            2.0        -
E-D3021E.1                       Ex. Data Visualization                                                        2.0        -

                                                                                      Totale ore settimanali: 4.0          -

Descrittivo dei corsi
Data Visualization
Codice                           C-D3021E.1

Obiettivi                        Acquire fundamental skills in data visualization in order to:
                                 - Understand how visual perception works and how it can be effectively used to
                                 amplify cognition
                                 - Understand the key design principles and techniques for visualizing data
                                 - Understand the different wide variety of data visualization techniques available
                                 - Identify the appropriate visualization methods for particular types of data and
                                 for a given problem
                                 - Develop effective data pre-processing and interactive data visualizations using
                                 a visualization tool (Tableau) and be
                                   able to argue for their effectiveness
                                 - Evaluate the quality of a visual representation

Contenuti                        History of visualization (who, why, when,…)
                                 Fundamentals of visualization
                                 - Theories and reference models
                                 - Data types
                                 - Perception
                                 - Interaction
                                 Visualization techniques (with practical applications with the tool)
                                 - Multivariate data visualizations
                                 - Network data visualizations
                                 - Hierarchical data visualizations
                                 Visualization applications (introduction)
                                 - Business intelligence
                                 - Visual analytics
                                 - Visual storytelling
                                 Evaluation of visual applications

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Hackathon 2
Codice                           M-D3030E.1

Crediti                          2.0 ECTS                          Semestre di riferimento            3°

Durata                           1 semestre                        Tipo di modulo                     Obbligatorio

Responsabile modulo              Huber David

Corsi
                                                                                                           Ore SA    Ore SP
E-D3031E.1                       Ex. Hackathon 2                                                               1.0        -

                                                                                      Totale ore settimanali: 1.0          -

Descrittivo dei corsi
Ex. Hackathon 2
Codice                           E-D3031E.1

Obiettivi                        Practice working as a team on a project touching topics that will be covered in
                                 the 3rd semester.

Contenuti                        Simple tasks motivated by real problems related to data management,
                                 visualization and simple handcrafted classification rules.

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Data Management
Codice                           M-I3060E.1

Crediti                          5.0 ECTS                          Semestre di riferimento            3°

Durata                           1 semestre                        Tipo di modulo                     Obbligatorio

Responsabile modulo              Mastropietro Roberto

Corsi
                                                                                                           Ore SA    Ore SP
C-I3061E.1                       Data Management                                                               2.0        -
E-I3061E.1                       Ex. Data Management                                                           4.0        -

                                                                                      Totale ore settimanali: 6.0          -

Descrittivo dei corsi
Data Management
Codice                           C-I3061E.1

Obiettivi                        Learn and use the functionalities offered by DBMS
                                 Learn and use different data management tools
                                 Learn and use cloud-based persistence systems
                                 Identify the best technologies for a given data management problem.

Contenuti                        Database vs DBMS
                                 DB Design, relational model
                                 SQL
                                 Indices
                                 NoSQL, Document-based, Key-value and Graph-based systems
                                 Blockchain data structure
                                 Introduction to Datawarehouses.

Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2021/2022                                  22 di 43
Dipartimento tecnologie innovative - 17/09/2021 19:50:20
Software Modelling with Applications
Codice                           M-I3070E.1

Crediti                          5.0 ECTS                          Semestre di riferimento            3°

Durata                           1 semestre                        Tipo di modulo                     Obbligatorio

Prerequisiti                     First year module group

Metodo di valutazione            Mid-term test
                                 Exam

Responsabile modulo              Pedrazzini Sandro

Corsi
                                                                                                           Ore SA    Ore SP
C-I3071E.1                       Software Modelling with Applications                                          2.0        -
E-I3071E.1                       Ex. Software Modelling with Applications                                      4.0        -

                                                                                      Totale ore settimanali: 6.0          -

Descrittivo dei corsi
Software Modelling with Applications
Codice                           C-I3071E.1

Obiettivi                        Understand Object Oriented programming and inheritance
                                 Organise code in packages and apply the principles of component-oriented
                                 programming for "in the large" programs
                                 Apply software engineering techniques in the code development process
                                 Practice and consolidate the current programming skills

Contenuti                        Object oriented programming (polymorphism, inheritance)
                                 Component oriented programming (web services)
                                 UML as design formalism
                                 Build process and dependencies
                                 Lab in Python:
                                 - Practice theoretical subjects in Python
                                 - Version control system
                                 - Testing
                                 - Webservices

Metodo di                        Frontal and interactive lessons
insegnamento                     Lab sessions

Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2021/2022                                  23 di 43
Dipartimento tecnologie innovative - 17/09/2021 19:50:20
Applied Operations Research
Codice                           M-B4050E.1

Crediti                          4.0 ECTS                          Semestre di riferimento          4°

Durata                           1 semestre                        Tipo di modulo                   Obbligatorio

Responsabile modulo              Salani Matteo

Corsi
                                                                                                         Ore SA    Ore SP
C-B4051E.1                       Applied Operations Research                                                  -        2.0
E-B4051E.1                       Ex. Applied Operations Research                                              -        2.0

                                                                                      Totale ore settimanali: -        4.0

Descrittivo dei corsi
Applied Operations Research
Codice                           C-B4051E.1

Obiettivi                        Analyse and understand how to formulate a problem and to identify a suitable
                                 optimisation technique
                                 Learn the major algorithms to solve flavours of mathematical programming
                                 problems
                                 Apply the learnt techniques to case studies of industrial problems (logistics and
                                 production planning).

Contenuti                        The course will present a thorough introduction to the fundamental algorithmic
                                 techniques of Discrete Mathematics:
                                 - Linear programming and the simplex algorithm
                                 - Integer programming and mixed integer programming
                                 - Network flow problems
                                 - Shortest Path Problem, Travelling Salesman problem, Vehicle routing problem.

Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2021/2022                                24 di 43
Dipartimento tecnologie innovative - 17/09/2021 19:50:20
Algorithm design
Codice                           M-B4060E.1

Crediti                          6.0 ECTS                          Semestre di riferimento          4°

Durata                           1 semestre                        Tipo di modulo                   Obbligatorio

Responsabile modulo              Mastrolilli Palmo Monaldo

Corsi
                                                                                                         Ore SA    Ore SP
C-B4061E.1                       Algorithm design                                                             -        4.0
E-B4061E.1                       Ex. Algorithm design                                                         -        4.0

                                                                                      Totale ore settimanali: -        8.0

Descrittivo dei corsi
Algorithm design
Codice                           C-B4061E.1

Obiettivi                        Acquire basic competencies in graph algorithms and graph optimisation
                                 Learn and study search algorithms
                                 Study heuristic and meta-heuristic algorithms.

Contenuti                        Basic computational complexity
                                 Basic graph algorithms: breadth-first and depth-first search
                                 Search algorithms, including non-informed search, heuristic search, and
                                 concurrent search
                                 Metaheuristics for optimisation: ant colony optimisation, simulated annealing,
                                 genetic algorithms

Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2021/2022                                25 di 43
Dipartimento tecnologie innovative - 17/09/2021 19:50:20
Unsupervised learning
Codice                           M-D4010E.1

Crediti                          4.0 ECTS                          Semestre di riferimento          4°

Durata                           1 semestre                        Tipo di modulo                   Obbligatorio

Responsabile modulo              Wand Michael

Corsi
                                                                                                         Ore SA    Ore SP
C-D4011E.1                       Unsupervised learning                                                        -        2.0
E-D4011E.1                       Ex. Unsupervised learning                                                    -        2.0

                                                                                      Totale ore settimanali: -        4.0

Descrittivo dei corsi
Unsupervised learning
Codice                           C-D4011E.1

Obiettivi                        Knowledge of the main unsupervised learning problems (clustering,
                                 recommendation, anomaly detection)
                                 Understanding of clutering methos
                                 Capability of selecting and using unsupervised learning algorithms to solve
                                 simple real-world problems and properly interpreting their results.

Contenuti                        Intro to unsupervised learning and clustering
                                 Clustering methods (hierarchical, kmeans)
                                 Recommender systems
                                 Anomaly and outlier detection
                                 Practical problems from key areas.

Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2021/2022                                26 di 43
Dipartimento tecnologie innovative - 17/09/2021 19:50:20
Vertical Domain Application in Key Areas
Codice                           M-D4020E.1

Crediti                          4.0 ECTS                          Semestre di riferimento          4°

Durata                           1 semestre                        Tipo di modulo                   Obbligatorio

Responsabile modulo              Piga Dario

Corsi
                                                                                                         Ore SA    Ore SP
C-D4021E.1                       Vertical Domain Application in Key Areas                                     -        4.0

                                                                                      Totale ore settimanali: -        4.0

Descrittivo dei corsi
Vertical Domain Application in Key Areas
Codice                           C-D4021E.1

Obiettivi                        Build up knowledge on how data science is being applied to a variety of real
                                 world application fields.

Contenuti                        Introduction to the key areas of vertical domain application such as fintech,
                                 industry 4.0, biotech (pharma), fashion industry, robotics (drones, autonomous
                                 vehicles), Internet of Things, social networks, mobility, energy, environment
                                 and climate change.
                                 Analysis and discussion with domain experts on case studies from selected
                                 applications.

Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2021/2022                                27 di 43
Dipartimento tecnologie innovative - 17/09/2021 19:50:20
Data Challenge: Annual Project in Applied
Data Science 2
Codice                           M-D4030E.1

Crediti                          4.0 ECTS                          Semestre di riferimento   4°

Durata                           1 semestre                        Tipo di modulo            Obbligatorio

Responsabile modulo              Corani Giorgio

Corsi
                                                                                                  Ore SA    Ore SP
E-D4031E.1                       Ex. Data Challenge: Annual Project in Applied                         -        2.0
                                 Data Science 2
                                                                         Totale ore settimanali: -              2.0

Descrittivo dei corsi
Ex. Data Challenge: Annual Project in Applied Data Science 2
Codice                           E-D4031E.1

Obiettivi                        Exercise programming and analysis skills by working on a real-world dataset,
                                 performing
                                 more advanced tasks of data analysis. The activities relies on the notions learned
                                 during up to third semester.

Contenuti                        The challenge will propose different tasks, including:
                                 Data collection
                                 Data preprocessing
                                 Data analysis (statistics / ML)
                                 Evaluation of results
                                 Presentation of the results in written form.

Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2021/2022                         28 di 43
Dipartimento tecnologie innovative - 17/09/2021 19:50:20
Hackathon 3
Codice                           M-D4040E.1

Crediti                          2.0 ECTS                          Semestre di riferimento          4°

Durata                           1 semestre                        Tipo di modulo                   Obbligatorio

Responsabile modulo              Huber David

Corsi
                                                                                                         Ore SA    Ore SP
E-D4041E.1                       Ex. Hackathon 3                                                              -        1.0

                                                                                      Totale ore settimanali: -        1.0

Descrittivo dei corsi
Ex. Hackathon 3
Codice                           E-D4041E.1

Obiettivi                        Practice working as a team on a project touching topics that will be covered in
                                 the 4th semester.

Contenuti                        Simple tasks motivated by real problems related to parallel programming, and
                                 operation research.

Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2021/2022                                29 di 43
Dipartimento tecnologie innovative - 17/09/2021 19:50:20
Parallel and Concurrent Programming
Codice                           M-I4070E.1

Crediti                          4.0 ECTS                          Semestre di riferimento          4°

Durata                           1 semestre                        Tipo di modulo                   Obbligatorio

Responsabile modulo              Leidi Tiziano

Corsi
                                                                                                         Ore SA    Ore SP
C-I4071E.1                       Parallel and Concurrent Programming                                          -        2.0
E-I4071E.1                       Ex. Parallel and Concurrent Programming                                      -        2.0

                                                                                      Totale ore settimanali: -        4.0

Descrittivo dei corsi
Parallel and Concurrent Programming
Codice                           C-I4071E.1

Obiettivi                        Understand the principles of parallel and concurrent programming in a language
                                 independent way. Know how to apply such techniques in an operating
                                 environment and programming language.
                                 Know how to develop concurrent applications based on shared memory,
                                 message passing, synchronous and asynchronous events.
                                 Understand the basics of modern parallelization infrastructures.
                                 Study classical parallelization problems.

Contenuti                        Theoretical concepts of concurrency and synchronization: atomicity, visibility,
                                 thread-safety, liveness, load balancing and scalability.
                                 Elements of concurrent programming: thread, lock
                                 Scheduling strategies with threads
                                 Principles pf parallel programming: models and tecniques for parallel processing
                                 Solutions for parallel programming in Python: thread-pools, TBD
                                 Classical synchronization problems
                                 GPUs programming principles.

Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2021/2022                                30 di 43
Dipartimento tecnologie innovative - 17/09/2021 19:50:20
Ethics, Law and Privacy in Data and Analytics
Codice                           M-C5020E.1

Crediti                          2.0 ECTS                          Semestre di riferimento            5°

Durata                           1 semestre                        Tipo di modulo                     Obbligatorio

Responsabile modulo

Corsi
                                                                                                           Ore SA    Ore SP
C-C5021E.1                       Ethics, Law and Privacy in Data and Analytics                                 2.0        -

                                                                                      Totale ore settimanali: 2.0          -

Descrittivo dei corsi
Ethics, Law and Privacy in Data and Analytics
Codice                           C-C5021E.1

Obiettivi                        Learn key technical, ethical, policy and legal terms and concepts related to DS
                                 Identify and assess the ethical and legal impacts of a given course of action in
                                 the data profession
                                 Learn techniques for protecting privacy in data-driven context/areas
                                 (organisations)
                                 Learn algorithmic and data-driven approaches for mitigating biases and
                                 enhancing fairness in AI/ML systems.

Contenuti                        What are ethics?
                                 Societal implications of data profession
                                 Data collection, representation and exclusion
                                 Data owernship
                                 Privacy and anonimity
                                 Fairness and bias
                                 Legal framework for the data profession.

Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2021/2022                                  31 di 43
Dipartimento tecnologie innovative - 17/09/2021 19:50:20
Advanced Machine Learning
Codice                           M-D5010E.1

Crediti                          4.0 ECTS                          Semestre di riferimento            5°

Durata                           1 semestre                        Tipo di modulo                     Obbligatorio

Responsabile modulo

Corsi
                                                                                                           Ore SA    Ore SP
C-D5011E.1                       Advanced Machine Learning                                                     2.0        -
E-D5011E.1                       Ex. Advanced Machine Learning                                                 2.0        -

                                                                                      Totale ore settimanali: 4.0          -

Descrittivo dei corsi
Advanced Machine Learning
Codice                           C-D5011E.1

Obiettivi                        Knowledge of important tasks in ML pipelines such as feature engineering,
                                 selection and dimensionality reduction
                                 Ability to handle and visualize high-dimensional data
                                 Balancing accuracy and complexity for real-world ML applications.

Contenuti                        Feature engineering methods
                                 Dimensionality reduction in the unsupervised case
                                 Feature selection for classification and regression
                                 Principal component analysis
                                 Hidden Markov Models for sequence data
                                 Practical problems from key areas.

Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2021/2022                                  32 di 43
Dipartimento tecnologie innovative - 17/09/2021 19:50:20
Deep Learning and Computer Vision
Codice                           M-D5020E.1

Crediti                          5.0 ECTS                          Semestre di riferimento            5°

Durata                           1 semestre                        Tipo di modulo                     Obbligatorio

Responsabile modulo

Corsi
                                                                                                           Ore SA    Ore SP
C-D5021E.1                       Deep Learning and Computer Vision                                             2.0        -
E-D5021E.1                       Ex. Deep Learning and Computer Vision                                         4.0        -

                                                                                      Totale ore settimanali: 6.0          -

Descrittivo dei corsi
Deep Learning and Computer Vision
Codice                           C-D5021E.1

Obiettivi                        Knowledge of basic deep learning concepts: neural networks, backpropagation
                                 Capability of applying common deep models for image classification
                                 (Convolutional Neural Networks) to real-world tasks
                                 Knowledge of other computer vision tasks solved by deep learning
                                 (segmentation, detection)

Contenuti                        Introduction to neural networks and deep learning
                                 Neural Networks: mathematical foundations
                                 Gradient-based optimization and backpropagation
                                 Deep learning for computer vision
                                 Deep Learning for sequences

Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2021/2022                                  33 di 43
Dipartimento tecnologie innovative - 17/09/2021 19:50:20
Bayesian Probabilistic Programming
Codice                           M-D5030E.1

Crediti                          4.0 ECTS                          Semestre di riferimento            5°

Durata                           1 semestre                        Tipo di modulo                     Obbligatorio

Responsabile modulo

Corsi
                                                                                                           Ore SA    Ore SP
C-D5031E.1                       Bayesian Probabilistic Programming                                            2.0        -
E-D5031E.1                       Ex. bayesian Probabilistic Programming                                        2.0        -

                                                                                      Totale ore settimanali: 4.0          -

Descrittivo dei corsi
Bayesian Probabilistic Programming
Codice                           C-D5031E.1

Obiettivi                        Konwledge of Bayesian inference
                                 Capability of defining a probabilistic model:
                                 - model definition
                                 - objective funcion definition
                                 - optimization method choice
                                 Capability of assessing the performance of a probabilistic model
                                 Capability of implementing simple probabilistic models

Contenuti                        Bayesian inference
                                 Probabilistic modelling:
                                 - model definition
                                 - objective funcion definition
                                 - optimization method choice
                                 Performance evaluation of probabilistic models:
                                 - accuracy
                                 - correctness of the numerical sampling
                                 - interpretation of the statistics
                                 Introduction of probabilistic programming languages
                                 Implementation of basic machine learning models:
                                 - simple regression models
                                 - hierarchical models
                                 - clustering (LDA)
                                 Practical problems from key areas.

Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2021/2022                                  34 di 43
Dipartimento tecnologie innovative - 17/09/2021 19:50:20
Applied Case Studies of Machine Learning
and Deep Learning in Key Areas
Codice                           M-D5040E.1

Crediti                          6.0 ECTS                          Semestre di riferimento   5°

Durata                           1 semestre                        Tipo di modulo            Obbligatorio

Responsabile modulo

Corsi
                                                                                                  Ore SA    Ore SP
C-D5041E.1                       Applied Case Studies of Machine Learning and                         4.0        -
                                 Deep Learning in Key Areas
E-D5041E.1                       Ex. Applied Case Studies of Machine Learning                 4.0                 -
                                 and Deep Learning in Key Areas
                                                                      Totale ore settimanali: 8.0                 -

Descrittivo dei corsi
Applied Case Studies of Machine Learning and Deep Learning in Key Areas
Codice                           C-D5041E.1

Obiettivi                        Expand knowledge on how data science is being applied to a variety of real world
                                 application fields.

Contenuti                        Provide solutions to case studies from the concerned key areas of applications

Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2021/2022                         35 di 43
Dipartimento tecnologie innovative - 17/09/2021 19:50:20
Data Challenge: Annual Project in Applied
Data Science 3
Codice                           M-D5050E.1

Crediti                          4.0 ECTS                          Semestre di riferimento   5°

Durata                           1 semestre                        Tipo di modulo            Obbligatorio

Responsabile modulo              Corani Giorgio

Corsi
                                                                                                  Ore SA    Ore SP
E-D5051E.1                       Ex. Data Challenge: Annual Project in Applied                        2.0        -
                                 Data Science 3
                                                                       Totale ore settimanali: 2.0                -

Descrittivo dei corsi
Ex. Data Challenge: Annual Project in Applied Data Science 3
Codice                           E-D5051E.1

Obiettivi                        Exercise programming and analysis skills by working on a real-world dataset,
                                 performing
                                 advanced tasks of data analysis.

Contenuti                        The challenge will propose different tasks, including:
                                 Data collection, prepocessing and analysis
                                 Evaluation of societal, ethical and legal implications of data analysis
                                 Presentation of the results in written form.

Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2021/2022                         36 di 43
Dipartimento tecnologie innovative - 17/09/2021 19:50:20
Hackathon 4
Codice                           M-D5060E.1

Crediti                          2.0 ECTS                          Semestre di riferimento            5°

Durata                           1 semestre                        Tipo di modulo                     Obbligatorio

Responsabile modulo              Huber David

Corsi
                                                                                                           Ore SA    Ore SP
E-D5061E.1                       Ex. Hackathon 4                                                               1.0        -

                                                                                      Totale ore settimanali: 1.0          -

Descrittivo dei corsi
Ex. Hackathon 4
Codice                           E-D5061E.1

Obiettivi                        Practice working as a team on a project touching topics covered in the 5th
                                 semester.

Contenuti                        Simple tasks motivated by real problems related to big data.

Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2021/2022                                  37 di 43
Dipartimento tecnologie innovative - 17/09/2021 19:50:20
Big Data Processing
Codice                           M-I5080E.1

Crediti                          4.0 ECTS                          Semestre di riferimento            5°

Durata                           1 semestre                        Tipo di modulo                     Obbligatorio

Responsabile modulo

Corsi
                                                                                                           Ore SA    Ore SP
C-I5081E.1                       Big Data Processing                                                           2.0        -
E-I5081E.1                       Ex. Big Data Processing                                                       2.0        -

                                                                                      Totale ore settimanali: 4.0          -

Descrittivo dei corsi
Big Data Processing
Codice                           C-I5081E.1

Obiettivi                        Introduction to Big Data problems and frameworks.

Contenuti                        Apache Ecosystem: Kafka, Hadoop, Spark, etc
                                 From MapReduce to Functional in Spark
                                 Cloud platforms for data analysis and machine learning
                                 Tableau

Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2021/2022                                  38 di 43
Dipartimento tecnologie innovative - 17/09/2021 19:50:20
Natural Language Processing and Text
Mining
Codice                           M-D6010E.1

Crediti                          4.0 ECTS                          Semestre di riferimento          6°

Durata                           1 semestre                        Tipo di modulo                   Obbligatorio

Responsabile modulo

Corsi
                                                                                                         Ore SA    Ore SP
C-D6011E.1                       Natural Language Processing and Text Mining                                  -        2.0
E-D6011E.1                       Ex. Natural Language Processing and Text Mining                              -        2.0

                                                                                      Totale ore settimanali: -        4.0

Descrittivo dei corsi
Natural Language Processing and Text Mining
Codice                           C-D6011E.1

Obiettivi                        The goal of this course is to provide the students with an in-depth understanding
                                 of the field of computational linguistics (a.k.a. NLP). We will cover the main
                                 problems taken into consideration by the field, such as text analysis, text
                                 classification, information extraction, dialogue, translation. We will present an
                                 historical overview of the field with a quick look at historical approaches, followed
                                 by a rich treatment of the most modern approaches (based on word embeddings
                                 and deep learning).

Contenuti                        Application areas:
                                 - Text classification, Information Extraction, Text Understanding, Question
                                 Answering, Dialogue, Text Generation, Translation
                                 Traditional sequential NLP pipeline modules:
                                 - Sentence splitting, tokenization, PoS tagging, lemmatization, chunking,
                                 syntactic parsing, co-reference resolution, rethorical structure
                                 Approaches based on classical ML:
                                 - Topic modelling, sentiment classification, n-gram language models, evaluation
                                 metrics
                                 Modern neural-based approaches:
                                 - RNNs, CNN, LSTMs, seq2seq, attention and the transformer architecture
                                 Word Embeddings:
                                 - word2vec, fastText, GloVe, Elmo, BERT

Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2021/2022                                39 di 43
Dipartimento tecnologie innovative - 17/09/2021 19:50:20
Time series, Analytics and Forecasting
Codice                           M-D6020E.1

Crediti                          4.0 ECTS                          Semestre di riferimento          6°

Durata                           1 semestre                        Tipo di modulo                   Obbligatorio

Responsabile modulo

Corsi
                                                                                                         Ore SA    Ore SP
C-D6021E.1                       Time series, Analytics and Forecasting                                       -        2.0
E-D6021E.1                       Ex. Time series, Analytics and Forecasting                                   -        2.0

                                                                                      Totale ore settimanali: -        4.0

Descrittivo dei corsi
Time series, Analytics and Forecasting
Codice                           C-D6021E.1

Obiettivi                        Knowledge of the most important forecasting algorithms
                                 Ability of making decisions based on probabilistic forecasting
                                 Ability of using forecasting libraries

Contenuti                        TS visualization
                                 Forecasting algorithms (exponential smoothing, arima) and automatic forecasting
                                 Practical problems from key areas (e.g.: hierarchical forecasting)HMM and RNNs
                                 for sequential data analysis, assuming that Deep learning concepts are already
                                 known
                                 Time series classification

Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2021/2022                                40 di 43
Dipartimento tecnologie innovative - 17/09/2021 19:50:20
Applied Case Studies of Machine Learning
and Deep Learning in Key Areas 2
Codice                           M-D6030E.1

Crediti                          4.0 ECTS                          Semestre di riferimento   6°

Durata                           1 semestre                        Tipo di modulo            Obbligatorio

Responsabile modulo

Corsi
                                                                                                  Ore SA    Ore SP
C-D6031E.1                       Applied Case Studies of Machine Learning and                          -        4.0
                                 Deep Learning in Key Areas 2
E-D6031E.1                       Ex. Applied Case Studies of Machine Learning                  -                4.0
                                 and Deep Learning in Key Areas 2
                                                                       Totale ore settimanali: -                8.0

Descrittivo dei corsi
Applied Case Studies of Machine Learning and Deep Learning in Key Areas 2
Codice                           C-D6031E.1

Obiettivi                        Expand knowledge on how data science is being applied to a variety of real world
                                 application fields.

Contenuti                        Provide solutions to case studies from the concerned key areas of applications.

Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2021/2022                         41 di 43
Dipartimento tecnologie innovative - 17/09/2021 19:50:20
Data Security and Blockchain
Codice                           M-I6040E.1

Crediti                          4.0 ECTS                          Semestre di riferimento          6°

Durata                           1 semestre                        Tipo di modulo                   Obbligatorio

Responsabile modulo

Corsi
                                                                                                         Ore SA    Ore SP
C-I6041E.1                       Data Security and Blockchain                                                 -        2.0
E-I6041E.1                       Ex. Data Security and Blockchain                                             -        2.0

                                                                                      Totale ore settimanali: -        4.0

Descrittivo dei corsi
Data Security and Blockchain
Codice                           C-I6041E.1

Obiettivi                        Understand the principles of ICT and data security (including blockhain).
                                 Study the fundamentals of modern cryptographic systems.
                                 Understand how security and data science can co-operate to improve
                                 reciprocally (the importance of ICT security for data science and the usefulness
                                 of data science for cybersecurity).

Contenuti                        Security of online information systems
                                 Security of protocols and authentication systems (PKI, HTTPS, ...)
                                 Cryptography and code theory
                                 Congruence arithmetic
                                 Enduser security (how to protect data and avoid loss)
                                 The application of data science techniques to the fight against cyber-criminality

Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2021/2022                                42 di 43
Dipartimento tecnologie innovative - 17/09/2021 19:50:20
Diploma Thesis
Codice                           M-P6080E.1

Crediti                          13.0 ECTS                         Semestre di riferimento          6°

Durata                           1 semestre                        Tipo di modulo                   Obbligatorio

Responsabile modulo

Corsi
                                                                                                                   Ore totali
P-P6081E.1                       Diploma Thesis                                                                            -

                                                                                      Totale ore settimanali: -             -

Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2021/2022                                  43 di 43
Dipartimento tecnologie innovative - 17/09/2021 19:50:20
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