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 2020/2021 Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2020/2021 1 di 42 Dipartimento tecnologie innovative - 25/01/2021 02:57:52
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
E-B1011E.1 Ex. Calculus 1 2.0 2.0
Totale ore settimanali: 2.0 2.0
Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2020/2021 2 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Linear 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
Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2020/2021 3 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Probability 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
Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2020/2021 4 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Numerical analyis and optimisation
Codice M-B1110.1
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-B1111.1 Numerical analysis and optimisation 2.0 2.0
E-B1111.1 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-B1111.1
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
Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2020/2021 5 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Inglese 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
Responsabile modulo Losa Stefano
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
Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2020/2021 6 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Communication and presentation
Codice M-C1040.1
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 -
Descrittivo dei corsi
Communication and presentation
Codice C-C1041E.1
Obiettivi Develop communication skills to manage relationships and presentations to
different stakeholders.
Contenuti Public speaking
Team working and soft skills
First impressione and image
Meeting and kick off
Networking
Negotiation
Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2020/2021 7 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Ethics and Artificial Intelligence
Codice M-D1010.1
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-D1011.1 Introduction to Artificial Intelligence 2.0 -
C-D1012.1 Ethics of Artificial Intelligence - 2.0
E-D1011.1 Ex. Introduction to Artificial Intelligence 1.0 -
E-D1012.1 Ethics of Artificial Intelligence - 1.0
Totale ore settimanali: 3.0 3.0
Descrittivo dei corsi
Introduction to Artificial Intelligence
Codice C-D1011.1
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
two main kinds of knowledge representation: the logic/symbolic approach and the
subsymbolic/connectionist approach.
Understand and explain the main differences between the major kinds of
machine learning problems, namely supervised learning, unsupervised learning
and reinforcement learning
Contenuti Brief history of Artificial Intelligence
What is an intelligent agent
GOFAI:
- solving by search
- knowledge representation and reasoning (logic);
- limitations (frame problem etc)
Connectionism:
- what is a neural network,
- training a network (basic ideas),
- limitations
First concepts (overview) of machine learning
- types and models
- use in Data Science
Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2020/2021 8 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Ethics of Artificial Intelligence
Codice C-D1012.1
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 ArtificiaI
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 AI be regulated?
Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2020/2021 9 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Operating 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, 2020/2021 10 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Introduction to Computer programming
Codice M-I1070.1
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
Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2020/2021 11 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Descrittivo 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
Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2020/2021 12 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Project 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
Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2020/2021 13 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Data 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
Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2020/2021 14 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Hackathon
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.
Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2020/2021 15 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Calculus 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
Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2020/2021 16 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Communication and Reporting
Codice M-C3060.1
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-C3061.1 Communication and Reporting 2.0 -
Totale ore settimanali: 2.0 -
Descrittivo dei corsi
Communication and Reporting
Codice C-C3061.1
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, ..)
Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2020/2021 17 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Supervised Learning
Codice M-D3010.1
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-D3011.1 Supervised learning 4.0 -
E-D3011.1 Ex. Supervised Learning 2.0 -
Totale ore settimanali: 6.0 -
Descrittivo dei corsi
Supervised learning
Codice C-D3011.1
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
Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2020/2021 18 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Data visualization
Codice M-D3020.1
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-D3021.1 Data visualization 2.0 -
E-D3021.1 Ex. Data visualization 2.0 -
Totale ore settimanali: 4.0 -
Descrittivo dei corsi
Data visualization
Codice C-D3021.1
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
Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2020/2021 19 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Hackathon 2
Codice M-D3030.1
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-D3031.1 Ex. Hackathon 2 1.0 -
Totale ore settimanali: 1.0 -
Descrittivo dei corsi
Ex. Hackathon 2
Codice E-D3031.1
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.
Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2020/2021 20 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Data Management
Codice M-I3060.1
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-I3061.1 Data Management 2.0 -
E-I3061.1 Ex. Data Management 4.0 -
Totale ore settimanali: 6.0 -
Descrittivo dei corsi
Data Management
Codice C-I3061.1
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, 2020/2021 21 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Software Modelling with Applications
Codice M-I3070.1
Crediti 5.0 ECTS Semestre di riferimento 3°
Durata 1 semestre Tipo di modulo Obbligatorio
Responsabile modulo Pedrazzini Sandro
Corsi
Ore SA Ore SP
C-I3071.1 Software Modelling with Applications 2.0 -
E-I3071.1 Ex. Software Modelling with Applications 4.0 -
Totale ore settimanali: 6.0 -
Descrittivo dei corsi
Software Modelling with Applications
Codice C-I3071.1
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.
Contenuti Object oriented programming (polymorphism, inheritance - Component oriented
programming (web services)
Elements of software engineering:
- Development methodologies (e.g. Agile)
- Software Requirements and UML (some diagrams: sequence + class)
- Software quality and testing
Web services
Bachelor of Science SUPSI in Data Science and Artificial Intelligence TP, 2020/2021 22 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Applied Operations Research
Codice M-B4050.1
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-B4051.1 Applied Operations Research - 2.0
E-B4051.1 Ex. Applied Operations Research - 2.0
Totale ore settimanali: - 4.0
Descrittivo dei corsi
Applied Operations Research
Codice C-B4051.1
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, 2020/2021 23 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Algorithm design
Codice M-B4060.1
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-B4061.1 Algorithm design - 4.0
E-B4061.1 Ex. Algorithm design - 4.0
Totale ore settimanali: - 8.0
Descrittivo dei corsi
Algorithm design
Codice C-B4061.1
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, 2020/2021 24 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Unsupervised learning
Codice M-D4010.1
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-D4011.1 Unsupervised learning - 2.0
E-D4011.1 Ex. Unsupervised learning - 2.0
Totale ore settimanali: - 4.0
Descrittivo dei corsi
Unsupervised learning
Codice C-D4011.1
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, 2020/2021 25 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Vertical Domain Application in Key Areas
Codice M-D4020.1
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-D4021.1 Vertical Domain Application in Key Areas - 4.0
Totale ore settimanali: - 4.0
Descrittivo dei corsi
Vertical Domain Application in Key Areas
Codice C-D4021.1
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, 2020/2021 26 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Data Challenge: Annual Project in Applied
Data Science 2
Codice M-D4030.1
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-D4031.1 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-D4031.1
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, 2020/2021 27 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Hackathon 3
Codice M-D4040.1
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-D4041.1 Ex. Hackathon 3 - 1.0
Totale ore settimanali: - 1.0
Descrittivo dei corsi
Ex. Hackathon 3
Codice E-D4041.1
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, 2020/2021 28 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Parallel and Concurrent Programming
Codice M-I4070.1
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-I4071.1 Parallel and Concurrent Programming - 2.0
E-I4071.1 Ex. Parallel and Concurrent Programming - 2.0
Totale ore settimanali: - 4.0
Descrittivo dei corsi
Parallel and Concurrent Programming
Codice C-I4071.1
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, 2020/2021 29 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Ethics, Law and Privacy in Data and Analytics
Codice M-C5020.1
Crediti 2.0 ECTS Semestre di riferimento 5°
Durata 1 semestre Tipo di modulo Obbligatorio
Responsabile modulo
Corsi
Ore SA Ore SP
C-C5021.1 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-C5021.1
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, 2020/2021 30 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Advanced Machine Learning
Codice M-D5010.1
Crediti 4.0 ECTS Semestre di riferimento 5°
Durata 1 semestre Tipo di modulo Obbligatorio
Responsabile modulo
Corsi
Ore SA Ore SP
C-D5011.1 Advanced Machine Learning 2.0 -
E-D5011.1 Ex. Advanced Machine Learning 2.0 -
Totale ore settimanali: 4.0 -
Descrittivo dei corsi
Advanced Machine Learning
Codice C-D5011.1
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, 2020/2021 31 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Deep Learning and Computer Vision
Codice M-D5020.1
Crediti 5.0 ECTS Semestre di riferimento 5°
Durata 1 semestre Tipo di modulo Obbligatorio
Responsabile modulo
Corsi
Ore SA Ore SP
C-D5021.1 Deep Learning and Computer Vision 2.0 -
E-D5021.1 Ex. Deep Learning and Computer Vision 4.0 -
Totale ore settimanali: 6.0 -
Descrittivo dei corsi
Deep Learning and Computer Vision
Codice C-D5021.1
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, 2020/2021 32 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Bayesian Probabilistic Programming
Codice M-D5030.1
Crediti 4.0 ECTS Semestre di riferimento 5°
Durata 1 semestre Tipo di modulo Obbligatorio
Responsabile modulo
Corsi
Ore SA Ore SP
C-D5031.1 Bayesian Probabilistic Programming 2.0 -
E-D5031.1 Ex. Bayesian Probabilistic Programming 2.0 -
Totale ore settimanali: 4.0 -
Descrittivo dei corsi
Bayesian Probabilistic Programming
Codice C-D5031.1
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, 2020/2021 33 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Applied Case Studies of Machine Learning
and Deep Learning in Key Areas
Codice M-D5040.1
Crediti 6.0 ECTS Semestre di riferimento 5°
Durata 1 semestre Tipo di modulo Obbligatorio
Responsabile modulo
Corsi
Ore SA Ore SP
C-D5041.1 Applied Case Studies of Machine Learning and 4.0 -
Deep Learning in Key Areas
E-D5041.1 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-D5041.1
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, 2020/2021 34 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Data Challenge: Annual Project in Applied
Data Science 3
Codice M-D5050.1
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-D5051.1 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-D5051.1
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, 2020/2021 35 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Hackathon 4
Codice M-D5060.1
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-D5061.1 Ex. Hackathon 4 1.0 -
Totale ore settimanali: 1.0 -
Descrittivo dei corsi
Ex. Hackathon 4
Codice E-D5061.1
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, 2020/2021 36 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Big Data Processing
Codice M-I5080.1
Crediti 4.0 ECTS Semestre di riferimento 5°
Durata 1 semestre Tipo di modulo Obbligatorio
Responsabile modulo
Corsi
Ore SA Ore SP
C-I5081.1 Big Data Processing 2.0 -
E-I5081.1 Ex. Big Data Processing 2.0 -
Totale ore settimanali: 4.0 -
Descrittivo dei corsi
Big Data Processing
Codice C-I5081.1
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, 2020/2021 37 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Natural Language Processing and Text
Mining
Codice M-D6010.1
Crediti 4.0 ECTS Semestre di riferimento 6°
Durata 1 semestre Tipo di modulo Obbligatorio
Responsabile modulo
Corsi
Ore SA Ore SP
C-D6011.1 Natural Language Processing and Text Mining - 2.0
E-D6011.1 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-D6011.1
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, 2020/2021 38 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Time series, Analytics and Forecasting
Codice M-D6020.1
Crediti 4.0 ECTS Semestre di riferimento 6°
Durata 1 semestre Tipo di modulo Obbligatorio
Responsabile modulo
Corsi
Ore SA Ore SP
C-D6021.1 Time series, Analytics and Forecasting - 2.0
E-D6021.1 Ex. Time series, Analytics and Forecasting - 2.0
Totale ore settimanali: - 4.0
Descrittivo dei corsi
Time series, Analytics and Forecasting
Codice C-D6021.1
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, 2020/2021 39 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Applied Case Studies of Machine Learning
and Deep Learning in Key Areas 2
Codice M-D6030.1
Crediti 4.0 ECTS Semestre di riferimento 6°
Durata 1 semestre Tipo di modulo Obbligatorio
Responsabile modulo
Corsi
Ore SA Ore SP
C-D6031.1 Applied Case Studies of Machine Learning and - 4.0
Deep Learning in Key Areas 2
E-D6031.1 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-D6031.1
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, 2020/2021 40 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Data Security and Blockchain
Codice M-I6040.1
Crediti 4.0 ECTS Semestre di riferimento 6°
Durata 1 semestre Tipo di modulo Obbligatorio
Responsabile modulo
Corsi
Ore SA Ore SP
C-I6041.1 Data Security and Blockchain - 2.0
E-I6041.1 Ex. Data Security and Blockchain - 2.0
Totale ore settimanali: - 4.0
Descrittivo dei corsi
Data Security and Blockchain
Codice C-I6041.1
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, 2020/2021 41 di 42
Dipartimento tecnologie innovative - 25/01/2021 02:57:52Diploma 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, 2020/2021 42 di 42
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