Lecture notes by the professor.

Fundamentals: Software architecture concepts; Architectural descriptions; Software qualities; The architecture definition process.
Software qualities and designing for software qualities: performance; modifiability; availability; veriafiability; scalability; interoperability; monitoring.
Architectural patterns: Domain Model; Domain Object; Layers; Pipes & Filters; other POSA patterns; hexagonal architecture.
Architecture of distributed systems; client/server, peer-to-peer, remote invocation; broker; asynchronous communication; messaging; components; component-based architecture; container.
Service-based architecture; services; service-based architecture; REST services; microservices.
DevOps and software delivery: environments and their management; cloud computing; virtual machines and system virtualization; containers and container-based virtualization; container orchestration; continuous delivery.
Middleware.

Luca Cabibbo
Architettura del Software: Strutture e Qualità
Edizioni Efesto, 2021
ISBN: 9788833812458

Internet structure and the relationship between Internet, the Data Centers, and the Cloud
The Italian National Strategic Center (Polo Strategico Nazionale): A big Data Center in construction
The Italian Public Connectivity System (Sistema Pubblico di Connettività): A set of big private networks connected among themselves and with the Internet
Generalities on routing algorithms for the fixed network infrastructure
Level 3 algorithms and protocols
Distance Vector algorithms
Link-State-Packet algorithms
Routing protocols
Level 2 algorithms and protocols
Spanning tree algorithm for switched networks
VLAN: virtual local area networks
Evolution of the spanning tree protocol
Software Defined Networks
Interdomain routing
Border Gateway Protocol
BGP pitfalls
BGP scalability
Internet hierarchy
Using the RIPE Stat service
Using the AS Rank service
Using Looking Glasses - the case of Hurricane Electric
Internet anomalies
MPLS-based routing of an ISP
TCP and efficient transmission techinques
TCP efficiency for interactive services
TCP congestion control
TCP self-clocking and the bandwidth-delay product
TCP AIMD (Additive-Increase Multiplicative-Decrease) behaviour
BBR TCP
Routing in the data centers
Web-based services: from Data centers to CDNs
Architectures, models, and algorithms for Web-based services
Local distribution
Global distribution
Content delivery networks
IPv6 protocols
Address space and basic aspects of IPv6
The ICMPv6 protocol
Source address selection e multihoming
IPv4-IPv6 Transition mechanisms

Slides provided by the teacher and downloadable day by day from the course website: http://www.dia.uniroma3.it/~impianti/
In order to download the slides a userid-password pair is necessary (ask the teacher at maurizio.patrignani@uniroma3.it)

Decision making process. Introduction to Integer Linear Programming (ILP): relation between ILP and LP, equivalent formulations, relaxations, totally unimodular matrices, standard techniques for ILP modelling. ILP formulations: plant location, investment problem, sequencing problems, network optimization, transportation problems, set covering, set partitioning, set packing, crew scheduling. Exact algorithms: Branch and Bound, Cutting Planes, dynamic programming. Exact algorithms for binary and integer knapsack problems. Optimization on graphs: matching, vertex cover, max flow, independent set, Eulerian graphs and bipartite graphs. Use of an ILP commercial solver.

[1] M. FISCHETTI, "LEZIONI DI RICERCA OPERATIVA", EDIZIONI LIBRERIA PROGETTO PADOVA, ITALIA, 1995. (CAP. 2, 5, PARTE DEL 6 E DEL 7).
[2] R. AHUJA, T. MAGNANTI, J. ORLIN, "NETWORK FLOWS", PRENTICE HALL, 1993. (PG. 189-191, 473-475, 494-496)
[3] Lecture notes

Overview on decision making and Decision Support Systems (DSS). Model Driven DSS.
Mathematical modeling (examples of LP, ILP, and NLP formulations). Basics on computational complexity.
Introduction to Business Analytics. Predictive analytics, optimal classification trees, examples.
Prescriptive analytics. Heuristic algorithms: constructive heuristics, local search, variable depth local search, Tabu Search, Simulated Annealing, genetic algorithms, GRASP, Iterated Local Search, Variable Neighborhood Search, Guided Local Search, Ant Colony Optimization, PSO, Scatter Search, Path relinking...
Robust Optimization.
Study of real world cases (optimization of the flows in the distribution of frozen food, optimization of staff shifts in hospital departments, optimial routing for the collection of material for laboratory analysis, optimal management of the warehouse of a company that deals with online sales, ....).

1. Modelli e metodi decisionali in condizioni di incertezza e rischio, di G. Ghiani, R. Musmanno (a cura di), McGraw-Hill Education, 2009.
2. Slides e notes given by the lecturer

Fundamentals of data communications and networking
Circuit and packet switching, connection-oriented and connectionless services, network topologies and classification. Personal area networks (PAN), local area networks (LAN), metropolitan area networks (MAN), wide area networks (WAN), storage area networks (SAN). Network architectures and protocols.
Signal characteristics and Fourier analysis. Baseband and passband transmissions. Pulse code modulation (PCM), frequency division multiplexing (FDM), time division multiplexing (TDM), code division multiplexing (CDM), orthogonal frequency division multiplexing (OFDM).

Cable communication systems
Twisted pairs, coaxial cables and optical fibers. Digital subscriber lines (DSL). connectors. Laser diodes, LEDs and photodetectors. Synchronous digital hierarchy (SDH), synchronous optical networks (SONET). Fiber optic communication systems. Attenuation, dispersion, amplification in fiber optic systems. Wavelength division multiplexing (WDM). Optical transport networks (OTNs). Automatically switched transport networks (ASTN). Generalized multi-protocol label switching (GMPLS). Fiber optic access networks; passive optical networks (PON); fiber to the X (FTTX) systems. Fiber Channel.

Wireless communication systems
Propagation of electromagnetic waves. Fading. Optical wireless systems. Microwave transmission systems. Antennas for wireless communications. IEEE 802.11 (Wi-Fi). Bluetooth. Bluetooth Low Energy (BLE). Long Range WAN (LoRaWAN).

Mobile communications systems
Evolution of mobile systems, 4G and 5G systems.

Network security
Security Dimensions and levels. Symmetric and asymmetric cryptography. Data encryption standard (DES) and advanced encryption standard (AES). Wireless equivalent privacy (WEP) and Wi-Fi protected access (WPA). Quantum Key Distribution (QKD).

Andrew S. Tanenbaum - Computer Networks – 6 edizione
Stamatios V. Kartalopoulos - Next Generation Intelligent Optical Networks
Mahbub Hassan - Wireless and Mobile Networking

Qubit, pairs of qubits, registries, logic functions, no cloning theorem, Hadamard operator, teleportation, reversible computations, Bernstein Vazirani algorithm, Shor algorithm, amplitude amplification and the Groover algorithm, complexity theory and quantum computing

Slides by the teacher.

Database technology: physical structures, transaction management, distributed architectures. Data bases for decision support, database performance analysis and modeling. Seminars on recent topics in database management.

P. Atzeni et al. Basi di dati 5/Ed. McGraw-Hill, 2018 (or any major database textbook, contact the instructor for advice)
D. Menasce et al. Performance by Design: Computer Capacity Planning by Example, 2004
Additional material available on the course site:
http://www.dia.uniroma3.it/~atzeni/didattica/BD/BDIIindex.html

Database technology: physical structures, transaction management, distributed architectures. Data bases for decision support, data warehousing. Seminars on recent topics in database management.

P. Atzeni et al. Basi di dati 5/Ed. McGraw-Hill, 2018 (or any major database textbook, contact the instructor for advice)
Additional material available on the course site:
http://www.dia.uniroma3.it/~atzeni/didattica/BD/BDIIindex.html

1. Introduction to the Course

- Areas of interest in Machine Learning.
- Potenzialità dei modelli e dei metodi di ML.

2. Regression

- Introduction to Linear Regression.
- Overfitting in Regression.
- Regularization: Ridge Regression.
- Feature Selection and Lasso.

3. Classification

- Logistic Regression for classification.
- Overfitting in Classification.
- Boosting. AdaBoost algorithm.
- Naïve Bayes.
- Support Vector Machines.

4. Clustering

- Algoritmi k-means e k-means++ algorithms
- Expectation Maximization.
- Clustering gerarchico.

5. Artificial Neural Networks

- Architecture of Artificial Neural Networks.
- Backpropagation Learning algorithm.
- Applications of Artificial Neural Networks.

6. Keras and TensorFlow environments

- Keras and TensorFlow languages for developing ML applications.
- GPU-based architectures. Nvidia Tesla and Volta GPUs.
- Using TensorFlow with GPU support.

7. Dimensionality Reduction

- Data compression and visualization.
- Principal Component Analysis (PCA).
- Choosing the number of principal components.
- Applications in Recommender Systems.

8. Reinforcement Learning

- Markov Decision Process.
- Dynamic Programming.
- Reinforcement Learning algorithms.

9. Introduction to Deep Learning

- Introduction to Deep Forward Networks.
- Notes on Convolutional Neural Networks (CNN).
- Notes on Generative Adversarial Networks (GAN).

10. Case Studies and Projects

Various case studies will be presented and projects will be proposed in which to apply the notions learned on various domains of interest. In particular, the topics covered may concern, among other things, applications of ML methods and techniques in the following areas:

• Social Media Analysis (sentiment analysis, fake news detection, fake users detection, ecc.)
• Financial Machine Learning (algorithmic trading, ecc.)
• Recommender Systems (social RecSys, cultural heritage RecSys, e-commerce RecSys, ecc.)
• Data Science (prediction functions per applicazioni pratiche, ecc.)
• Computer Vision (object detection, face detection, face recognition, content-based video analysis, ecc.)
• Bioinformatics (recognition of genetic sequences, ecc.)

Lecture notes by the professor.

Chapter and section numbers are taken from the basic textbook S. Ross "INTRODUCTION TO PROBABILITY AND STATISTICS FOR ENGINEERS AND SCIENTISTS"

Cap. 1: 1.1, 1.2, 1.3
Cap. 2: 2.2, 2.2.1, 2.2.2, 2.3, 2.3.1, 2.3.2, 2.4, 2.5, 2.6
Cap. 3: 3.1, 3.2, 3.3, 3.4, 3.5, 3.5.1, 3.6, 3.7, 3.8
Cap. 4: 4.1, 4.2, 4.3, 4.3.1, 4.3.2, 4.3.3, 4.4, 4.5, 4.5.1, 4.6, 4.7, 4.9
Cap. 5: 5.1, 5.2, 5.4, 5.5, 5.6, 5.6.1, 5.7, 5.8.1., 5.8.2.
Cap. 6: 6.1, 6.2, 6.3, 6.3.1, 6.3.2, 6.4, 6.5, 6.5.1, 6.5.2
Cap. 7: 7.1, 7.2, 7.3, 7.3.1, 7.3.2, 7.4, 7.5, 7.7
Cap. 8: 8.1, 8.2, 8.3, 8.3.1, 8.3.2, 8.4.1, 8.6,
Cap. 9: 9.1, 9.2, 9.3, 9.6
Cap. 11: 11.1, 11.2, 11.4 (for this part you may also look at chapters 6.4, 6.5 of the book (in Italian) "Moduli di matematica e statistica con uso di R" di S. Invernizzi, M. Rinaldi e F. Comoglio).

- Sheldon Ross NTRODUCTION TO PROBABILITY AND STATISTICS FOR ENGINEERS AND SCIENTISTS
- "Moduli di matematica e statistica" di S. Invernizzi, M. Rinaldi e F. Comoglio, ed. Zanichelli.

additional reference: Luca Leuzzi, Enzo Marinari, Giorgio Parisi
CALCOLO DELLE PROBABILITÀ: un trattatello per principianti volenterosi

• Course introduction
• Introduction to computer security and terminology
• Vulneability and threats
◦ Software vulnerabilities. Trusted and untrusted input, input validation. Vulnerabilities of applications written with interpreted languages, code injection. Injection into web pages: XSS. Cross site request forgery. OWASP.
▪ Example of web site that is vulnerable to sql injection
◦ buffer overflow attacks. Exploitation: privilege excalation, intrusions through opens services, intrusions through untrusted documents (email, web, etc).
▪ Example of vulnerable code, buffer overflow and related exploit
◦ Vulnerabilities of networks: sniffing, mac flood, ARP poisoning, vulnerability of DNS, Kaminsky attack. TCP session hijecking, MitM attack, DoS and Distributed DoS, Route hijacking.
• Security planning : security plan content, risk analysis.
• Countermeasures
◦ Design principles of policies and mechanisms.
◦ Models: AAA, confinement, DAC, MAC, access control matrix 
◦ Cryptographic techniques:
▪ critptography basics (hash, symmetric c., asymmetric c., MAC, digital signature), birthday attack, rainbow, key quality, pseudo-random number generators.
▪ Authentication protocols and key exchange. replay and reflection attacks. Nonces. Perfect Forward Secrecy. Diffie-Helman. 
▪ Certificates, certification authority, public key infrastructures and their vulnerabilities.
▪ Applications: Protocols ssl, tls, ssh, virtual private networks, ipsec, etc. Autnetication protocols wan and lan. radius and vulnerabilities. Other applications.
◦ Anomaly detection systems.
◦ System security:
▪ general principles: passwords and their vulnerabilities, hardening, assessment and auditing
▪ unix: discertionaly access control, file system security, authentication, PAM, syslog
◦ Network security:
▪ Firewalling:stateless and statefull firewall, connections, syn-proxy and syn-cookies, load balancing and high availability, linux netfilter and configuration examples.
▪ Network siecurity at level 1 and 2.
▪ Applicative proxies and network intrusion detection systems .
• Authenticated Data Structures
• Distributed Ledger Technologies and Bitcoin
• Smart contracts
• Cybersecurity in big organizations.

Course handouts

1) Algorithms for data streams
- Approximate counting
- Majority problems
- Sampling and reservoir sampling
- Bloom filters
- Frequent itemsets
- Number of distinct elements
2) Algorithms and data structures for quantitative features analysis
- orthogonal range searching (kd-trees, range trees, and layered range trees)
- median finding
- multidimensional divide and conquer, closest pair
- fractional cascading
3) Algorithms for the decomposition of complex networks
- Decomposition into k-connected components
- Decomposition into k-cores, maximal cliques, maximal k-plexes
4) Locality sensitive hashing for finding similar items
- Min-Hashing
- Nearest neighbour search, k-nearest neighbour search

Slides plus:
Mining of Massive Datasets
Jure Leskovec, Anand Rajaraman, Jeff Ullman
Cambridge University Press
http://www.mmds.org/

1) Algorithms for data streams
- Approximate counting
- Majority problems
- Sampling and reservoir sampling
- Bloom filters
- Frequent itemsets
- Number of distinct elements
2) Algorithms and data structures for quantitative features analysis
- orthogonal range searching (kd-trees, range trees, and layered range trees)
- median finding
- multidimensional divide and conquer, closest pair
- fractional cascading
3) Locality sensitive hashing for finding similar items
- Min-Hashing
- Nearest neighbour search, k-nearest neighbour search
4) NoSQL internals & Distributed Hash Tables
- Chord
- consistent hashing
- Kademlia
5) Scalable security:
- integrity of big data sets in the cloud,
- consistency and scalability issues with authenticated data structures

Mining of Massive Datasets
Jure Leskovec, Anand Rajaraman, Jeff Ullman
Cambridge University Press
http://www.mmds.org/

1) Algorithms for data streams
- Approximate counting
- Majority problems
- Sampling and reservoir sampling
- Bloom filters
- Frequent itemsets
- Number of distinct elements
2) Algorithms and data structures for quantitative features analysis
- orthogonal range searching (kd-trees, range trees, and layered range trees)
- median finding
- multidimensional divide and conquer, closest pair
- fractional cascading
3) Locality sensitive hashing for finding similar items
- Min-Hashing
- Nearest neighbour search, k-nearest neighbour search
4) NoSQL internals & Distributed Hash Tables
- Chord
- consistent hashing
- Kademlia
5) Scalable security:
- integrity of big data sets in the cloud,
- consistency and scalability issues with authenticated data structures

Mining of Massive Datasets
Jure Leskovec, Anand Rajaraman, Jeff Ullman
Cambridge University Press
http://www.mmds.org/

1) Algorithms for data streams
- Approximate counting
- Majority problems
- Sampling and reservoir sampling
- Bloom filters
- Frequent itemsets
- Number of distinct elements
2) Algorithms and data structures for quantitative features analysis
- orthogonal range searching (kd-trees, range trees, and layered range trees)
- median finding
- multidimensional divide and conquer, closest pair
- fractional cascading
3) Locality sensitive hashing for finding similar items
- Min-Hashing
- Nearest neighbour search, k-nearest neighbour search
4) NoSQL internals & Distributed Hash Tables
- Chord
- consistent hashing
- Kademlia
5) Scalable security:
- integrity of big data sets in the cloud,
- consistency and scalability issues with authenticated data structures

Mining of Massive Datasets
Jure Leskovec, Anand Rajaraman, Jeff Ullman
Cambridge University Press
http://www.mmds.org/

Source discovery
Data quality
Data integration

Slides by the teacher

Hardware architectures; Software architectures; OpenGL, Vulkan; CUDA and OpenCL; Curves, surfaces, volumes; Algorithms for computer graphics and animation; Base concepts of physics-based animation.

Official documentations
- Unreal Game Engine:
- Vulkan
- DirectX
- OpenGL
- CUDA, OpenCL

- Introduction to CPS: applications and evolution
- Modeling CPS
- Communication standards and technologies for CPS
- Real time system
- Fault diagnosis: model based detection-isolation-identification of anomalies
- Model based anomaly detection for cyber attacks

Notes of the course
Scientific papers

Introduction to DL; Training of Deep Architecture: hyperparameter tuning, batch normalization, faster optimizers, regularization ; Convolutional Neural Networks (CNN/ConvNets); Recurrent Neural Networks (GRU, LSTM, Bidirectional); Encoder-Decoder, Autoencoders, Variational Autoencoders; Attention layers ; Generative Adversarial Networks (GAN); Deep Reinforcement Learning; Embeddings; Use cases: AlexNet, VGG, NiN, GoogLeNet/Inception, ResNet, DenseNet; Use cases: Computer Vision and NLP

Simon J.D. Prince. "Understanding Deep Learning". MIT Press Dec 5th 2023 A. Geron,
“Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems”, O'Reilly Media, Inc, USA, 2019.

The course will examine various methods for designing, implementing, and testing adaptive systems on the Web, realized through Artificial Intelligence techniques. We will pay particular attention to Information Retrieval systems, such as search engines, and new and emerging technologies suitable for developing the next generation of intelligent and personalized search tools. We will study classical retrieval models, such as the vector space and probabilistic models, document ranking techniques, as well as the PageRank algorithm adopted by Google. Algorithms of Machine Learning in Information Retrieval will be addressed, including Sentiment Analysis techniques, User Modeling methods needed for developing personalized research tools, Recommender Systems, and the detection and analysis of Online Communities and Social Networks (such as Facebook and Twitter).

Lectures will cover topics dealt with in scientific papers and reference texts.
The teacher will make slides from the lectures available through the course website. Those slides will be self-contained, that is, written in such a way as not to require the consultation of other material for passing the exam.

- Infrastructures and programming paradigms for big data
- The Hadoop Ecosystem
- Cloud computing
- Big data processing (MapReduce, Hive, Spark)
- NoSQL systems
- Big data analytics
- Data lakes
- Systems and applications
- Business seminars

Martin J. Fowler, PramodkumarJ. Sadalage. "NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence".
Teacher slides (available on the Web side of the course on Moodle)

The program is defined at the beginning of the course on the basis of the invited foreign professors. The website of the course (available on Moodle) is always kept up to date with the latest information.

Teaching materials and texts are chosen by foreign professors and will be published on the site of the course on Moodle.

Data and Visualization: Data overloading. Comparison of Scientific Visualization and Information Visualization. Structured and Unstructured data. Data transformation. Data tables.

Visual Perception: Our vision’s principles and limitations. Peripheral and central view. The perception of color.

Cognitive Issues and User Tasks: Perception abilities. Weber's law. Stevens' power law. Gestalt laws. A two stage model for visual perception. Task taxonomies.

Infovis on the Web - SVG and D3.js: Basic ingredients of Web data visualization. JavaScript crash course. Raster and vector graphics. Overview of JavaScript libraries. Focus on D3.js.

Multivariate Data Representations: Combined views. Icons or glyphs. Alternative coordinate systems.

Visualization in Computer Networks: Visual analysis in the computer network domain. Motivations. Taxonomies. Real-world examples and use cases. Open questions.

Design Methods and Evaluation: Design methodologies and design choices. Design evaluation (goals, difficulties, practices, guidelines).

Visualization of Time Series Data: Definition of time series and temporal data. Visualization of time series (single dependent variable, multiple dependent variables). Case studies.

Interaction: Classification of interaction mechanisms, goals, and timings. Examples of interaction strategies.

Introduction to Graph Drawing: Graph Drawing conventions and aesthetics. The divide an conquer approach for testing planarity of a graph.

Node-link Representations of Trees: Representing trees within the node-link paradigm. Layered drawings of trees. Hv-drawings of trees. Limitations of node-link representations.

Space-Filling Visualizations of Trees: Algorithms and systems for the representation of trees using the space-filling strategy. Treemaps. 3D Space-filling approaches.

Representations of Graphs and Networks with the Force-Directed Approach: The force-directed paradigm. The barycenter method. Spring embedders. Scalability and flexibility of the force-directed paradigm. Fruchterman-Reingold and Barnes–Hut algorithms. Simulating graph theoretic distances. Magnetic fields. Generic energy functions. Handling drawing constraints.

Representations of Hierarchical Data: Algorithms for the representation of layered networks. The Sugiyama approach. Step 1: Cycle removal. Step 2: Level Assignment. Step 3: Crossing Reduction. Step 4: X-Coordinate Assignment

Orthogonal Drawings: Computing orthogonal drawings via Network Flows. The Topology-Shape-Metric approach. Extension to graphs of arbitrary degree. Representations of orthogonal drawings obtained from visibility representations and by incremental approaches.

Visualizing Large Graphs: Strategies for the visualization of massive amount of data providing both overview and details. Alternate between views. Combine different views. Filtering and clustering principles. Three-dimensional and two-dimensional representations of clustered graphs. Hybrid representations.

Tools and Libraries for Drawing Graphs: Tools and Libraries for drawing graphs. Programming languages, input and output formats, and interaction. Some practical example.

Architectures for Scalable Information Visualization: Computational and memory scalabity. Visualization architectures. Strategies for visualizing massive amounts of data.

Slides provided by the teacher and downloadable day by day from the course website: http://www.dia.uniroma3.it/~infovis/
In order to download the slides a userid-password pair is necessary (ask the teacher at maurizio.patrignani@uniroma3.it)

Decision making process. Introduction to Integer Linear Programming (ILP): relation between ILP and LP, equivalent formulations, relaxations, totally unimodular matrices, standard techniques for ILP modelling. ILP formulations: plant location, investment problem, sequencing problems, network optimization, transportation problems, set covering, set partitioning, set packing, crew scheduling. Exact algorithms: Branch and Bound, Cutting Planes, dynamic programming. Exact algorithms for binary and integer knapsack problems. Optimization on graphs: matching, vertex cover, max flow, independent set, Eulerian graphs and bipartite graphs. Use of an ILP commercial solver.

[1] M. FISCHETTI, "LEZIONI DI RICERCA OPERATIVA", EDIZIONI LIBRERIA PROGETTO PADOVA, ITALIA, 1995. (CAP. 2, 5, PARTE DEL 6 E DEL 7).
[2] R. AHUJA, T. MAGNANTI, J. ORLIN, "NETWORK FLOWS", PRENTICE HALL, 1993. (PG. 189-191, 473-475, 494-496)
[3] Lecture notes

Overview on decision making and Decision Support Systems (DSS). Model Driven DSS.
Mathematical modeling (examples of LP, ILP, and NLP formulations). Basics on computational complexity.
Introduction to Business Analytics. Predictive analytics, optimal classification trees, examples.
Prescriptive analytics. Heuristic algorithms: constructive heuristics, local search, variable depth local search, Tabu Search, Simulated Annealing, genetic algorithms, GRASP, Iterated Local Search, Variable Neighborhood Search, Guided Local Search, Ant Colony Optimization, PSO, Scatter Search, Path relinking...
Robust Optimization.
Study of real world cases (optimization of the flows in the distribution of frozen food, optimization of staff shifts in hospital departments, optimial routing for the collection of material for laboratory analysis, optimal management of the warehouse of a company that deals with online sales, ....).

1. Modelli e metodi decisionali in condizioni di incertezza e rischio, di G. Ghiani, R. Musmanno (a cura di), McGraw-Hill Education, 2009.
2. Slides e notes given by the lecturer

Fundamentals of data communications and networking
Circuit and packet switching, connection-oriented and connectionless services, network topologies and classification. Personal area networks (PAN), local area networks (LAN), metropolitan area networks (MAN), wide area networks (WAN), storage area networks (SAN). Network architectures and protocols.
Signal characteristics and Fourier analysis. Baseband and passband transmissions. Pulse code modulation (PCM), frequency division multiplexing (FDM), time division multiplexing (TDM), code division multiplexing (CDM), orthogonal frequency division multiplexing (OFDM).

Cable communication systems
Twisted pairs, coaxial cables and optical fibers. Digital subscriber lines (DSL). connectors. Laser diodes, LEDs and photodetectors. Synchronous digital hierarchy (SDH), synchronous optical networks (SONET). Fiber optic communication systems. Attenuation, dispersion, amplification in fiber optic systems. Wavelength division multiplexing (WDM). Optical transport networks (OTNs). Automatically switched transport networks (ASTN). Generalized multi-protocol label switching (GMPLS). Fiber optic access networks; passive optical networks (PON); fiber to the X (FTTX) systems. Fiber Channel.

Wireless communication systems
Propagation of electromagnetic waves. Fading. Optical wireless systems. Microwave transmission systems. Antennas for wireless communications. IEEE 802.11 (Wi-Fi). Bluetooth. Bluetooth Low Energy (BLE). Long Range WAN (LoRaWAN).

Mobile communications systems
Evolution of mobile systems, 4G and 5G systems.

Network security
Security Dimensions and levels. Symmetric and asymmetric cryptography. Data encryption standard (DES) and advanced encryption standard (AES). Wireless equivalent privacy (WEP) and Wi-Fi protected access (WPA). Quantum Key Distribution (QKD).

Andrew S. Tanenbaum - Computer Networks – 6 edizione
Stamatios V. Kartalopoulos - Next Generation Intelligent Optical Networks
Mahbub Hassan - Wireless and Mobile Networking

Fundamentals: Software architecture concepts; Architectural descriptions; Software qualities; The architecture definition process.
Software qualities and designing for software qualities: performance; modifiability; availability; veriafiability; scalability; interoperability; monitoring.
Architectural patterns: Domain Model; Domain Object; Layers; Pipes & Filters; other POSA patterns; hexagonal architecture.
Architecture of distributed systems; client/server, peer-to-peer, remote invocation; broker; asynchronous communication; messaging; components; component-based architecture; container.
Service-based architecture; services; service-based architecture; REST services; microservices.
DevOps and software delivery: environments and their management; cloud computing; virtual machines and system virtualization; containers and container-based virtualization; container orchestration; continuous delivery.
Middleware.

Luca Cabibbo
Architettura del Software: Strutture e Qualità
Edizioni Efesto, 2021
ISBN: 9788833812458

Lecture notes by the professor.

Internet structure and the relationship between Internet, the Data Centers, and the Cloud
The Italian National Strategic Center (Polo Strategico Nazionale): A big Data Center in construction
The Italian Public Connectivity System (Sistema Pubblico di Connettività): A set of big private networks connected among themselves and with the Internet
Generalities on routing algorithms for the fixed network infrastructure
Level 3 algorithms and protocols
Distance Vector algorithms
Link-State-Packet algorithms
Routing protocols
Level 2 algorithms and protocols
Spanning tree algorithm for switched networks
VLAN: virtual local area networks
Evolution of the spanning tree protocol
Software Defined Networks
Interdomain routing
Border Gateway Protocol
BGP pitfalls
BGP scalability
Internet hierarchy
Using the RIPE Stat service
Using the AS Rank service
Using Looking Glasses - the case of Hurricane Electric
Internet anomalies
MPLS-based routing of an ISP
TCP and efficient transmission techinques
TCP efficiency for interactive services
TCP congestion control
TCP self-clocking and the bandwidth-delay product
TCP AIMD (Additive-Increase Multiplicative-Decrease) behaviour
BBR TCP
Routing in the data centers
Web-based services: from Data centers to CDNs
Architectures, models, and algorithms for Web-based services
Local distribution
Global distribution
Content delivery networks
IPv6 protocols
Address space and basic aspects of IPv6
The ICMPv6 protocol
Source address selection e multihoming
IPv4-IPv6 Transition mechanisms

Slides provided by the teacher and downloadable day by day from the course website: http://www.dia.uniroma3.it/~impianti/
In order to download the slides a userid-password pair is necessary (ask the teacher at maurizio.patrignani@uniroma3.it)

Chapter and section numbers are taken from the basic textbook S. Ross "INTRODUCTION TO PROBABILITY AND STATISTICS FOR ENGINEERS AND SCIENTISTS"

Cap. 1: 1.1, 1.2, 1.3
Cap. 2: 2.2, 2.2.1, 2.2.2, 2.3, 2.3.1, 2.3.2, 2.4, 2.5, 2.6
Cap. 3: 3.1, 3.2, 3.3, 3.4, 3.5, 3.5.1, 3.6, 3.7, 3.8
Cap. 4: 4.1, 4.2, 4.3, 4.3.1, 4.3.2, 4.3.3, 4.4, 4.5, 4.5.1, 4.6, 4.7, 4.9
Cap. 5: 5.1, 5.2, 5.4, 5.5, 5.6, 5.6.1, 5.7, 5.8.1., 5.8.2.
Cap. 6: 6.1, 6.2, 6.3, 6.3.1, 6.3.2, 6.4, 6.5, 6.5.1, 6.5.2
Cap. 7: 7.1, 7.2, 7.3, 7.3.1, 7.3.2, 7.4, 7.5, 7.7
Cap. 8: 8.1, 8.2, 8.3, 8.3.1, 8.3.2, 8.4.1, 8.6,
Cap. 9: 9.1, 9.2, 9.3, 9.6
Cap. 11: 11.1, 11.2, 11.4 (for this part you may also look at chapters 6.4, 6.5 of the book (in Italian) "Moduli di matematica e statistica con uso di R" di S. Invernizzi, M. Rinaldi e F. Comoglio).

- Sheldon Ross NTRODUCTION TO PROBABILITY AND STATISTICS FOR ENGINEERS AND SCIENTISTS
- "Moduli di matematica e statistica" di S. Invernizzi, M. Rinaldi e F. Comoglio, ed. Zanichelli.

additional reference: Luca Leuzzi, Enzo Marinari, Giorgio Parisi
CALCOLO DELLE PROBABILITÀ: un trattatello per principianti volenterosi

1. Introduction to the Course

- Areas of interest in Machine Learning.
- Potenzialità dei modelli e dei metodi di ML.

2. Regression

- Introduction to Linear Regression.
- Overfitting in Regression.
- Regularization: Ridge Regression.
- Feature Selection and Lasso.

3. Classification

- Logistic Regression for classification.
- Overfitting in Classification.
- Boosting. AdaBoost algorithm.
- Naïve Bayes.
- Support Vector Machines.

4. Clustering

- Algoritmi k-means e k-means++ algorithms
- Expectation Maximization.
- Clustering gerarchico.

5. Artificial Neural Networks

- Architecture of Artificial Neural Networks.
- Backpropagation Learning algorithm.
- Applications of Artificial Neural Networks.

6. Keras and TensorFlow environments

- Keras and TensorFlow languages for developing ML applications.
- GPU-based architectures. Nvidia Tesla and Volta GPUs.
- Using TensorFlow with GPU support.

7. Dimensionality Reduction

- Data compression and visualization.
- Principal Component Analysis (PCA).
- Choosing the number of principal components.
- Applications in Recommender Systems.

8. Reinforcement Learning

- Markov Decision Process.
- Dynamic Programming.
- Reinforcement Learning algorithms.

9. Introduction to Deep Learning

- Introduction to Deep Forward Networks.
- Notes on Convolutional Neural Networks (CNN).
- Notes on Generative Adversarial Networks (GAN).

10. Case Studies and Projects

Various case studies will be presented and projects will be proposed in which to apply the notions learned on various domains of interest. In particular, the topics covered may concern, among other things, applications of ML methods and techniques in the following areas:

• Social Media Analysis (sentiment analysis, fake news detection, fake users detection, ecc.)
• Financial Machine Learning (algorithmic trading, ecc.)
• Recommender Systems (social RecSys, cultural heritage RecSys, e-commerce RecSys, ecc.)
• Data Science (prediction functions per applicazioni pratiche, ecc.)
• Computer Vision (object detection, face detection, face recognition, content-based video analysis, ecc.)
• Bioinformatics (recognition of genetic sequences, ecc.)

Lecture notes by the professor.

1) Algorithms for data streams
- Approximate counting
- Majority problems
- Sampling and reservoir sampling
- Bloom filters
- Frequent itemsets
- Number of distinct elements
2) Algorithms and data structures for quantitative features analysis
- orthogonal range searching (kd-trees, range trees, and layered range trees)
- median finding
- multidimensional divide and conquer, closest pair
- fractional cascading
3) Algorithms for the decomposition of complex networks
- Decomposition into k-connected components
- Decomposition into k-cores, maximal cliques, maximal k-plexes
4) Locality sensitive hashing for finding similar items
- Min-Hashing
- Nearest neighbour search, k-nearest neighbour search

Slides plus:
Mining of Massive Datasets
Jure Leskovec, Anand Rajaraman, Jeff Ullman
Cambridge University Press
http://www.mmds.org/

1) Algorithms for data streams
- Approximate counting
- Majority problems
- Sampling and reservoir sampling
- Bloom filters
- Frequent itemsets
- Number of distinct elements
2) Algorithms and data structures for quantitative features analysis
- orthogonal range searching (kd-trees, range trees, and layered range trees)
- median finding
- multidimensional divide and conquer, closest pair
- fractional cascading
3) Locality sensitive hashing for finding similar items
- Min-Hashing
- Nearest neighbour search, k-nearest neighbour search
4) NoSQL internals & Distributed Hash Tables
- Chord
- consistent hashing
- Kademlia
5) Scalable security:
- integrity of big data sets in the cloud,
- consistency and scalability issues with authenticated data structures

Mining of Massive Datasets
Jure Leskovec, Anand Rajaraman, Jeff Ullman
Cambridge University Press
http://www.mmds.org/

1) Algorithms for data streams
- Approximate counting
- Majority problems
- Sampling and reservoir sampling
- Bloom filters
- Frequent itemsets
- Number of distinct elements
2) Algorithms and data structures for quantitative features analysis
- orthogonal range searching (kd-trees, range trees, and layered range trees)
- median finding
- multidimensional divide and conquer, closest pair
- fractional cascading
3) Locality sensitive hashing for finding similar items
- Min-Hashing
- Nearest neighbour search, k-nearest neighbour search
4) NoSQL internals & Distributed Hash Tables
- Chord
- consistent hashing
- Kademlia
5) Scalable security:
- integrity of big data sets in the cloud,
- consistency and scalability issues with authenticated data structures

Mining of Massive Datasets
Jure Leskovec, Anand Rajaraman, Jeff Ullman
Cambridge University Press
http://www.mmds.org/

1) Algorithms for data streams
- Approximate counting
- Majority problems
- Sampling and reservoir sampling
- Bloom filters
- Frequent itemsets
- Number of distinct elements
2) Algorithms and data structures for quantitative features analysis
- orthogonal range searching (kd-trees, range trees, and layered range trees)
- median finding
- multidimensional divide and conquer, closest pair
- fractional cascading
3) Locality sensitive hashing for finding similar items
- Min-Hashing
- Nearest neighbour search, k-nearest neighbour search
4) NoSQL internals & Distributed Hash Tables
- Chord
- consistent hashing
- Kademlia
5) Scalable security:
- integrity of big data sets in the cloud,
- consistency and scalability issues with authenticated data structures

Mining of Massive Datasets
Jure Leskovec, Anand Rajaraman, Jeff Ullman
Cambridge University Press
http://www.mmds.org/

• Course introduction
• Introduction to computer security and terminology
• Vulneability and threats
◦ Software vulnerabilities. Trusted and untrusted input, input validation. Vulnerabilities of applications written with interpreted languages, code injection. Injection into web pages: XSS. Cross site request forgery. OWASP.
▪ Example of web site that is vulnerable to sql injection
◦ buffer overflow attacks. Exploitation: privilege excalation, intrusions through opens services, intrusions through untrusted documents (email, web, etc).
▪ Example of vulnerable code, buffer overflow and related exploit
◦ Vulnerabilities of networks: sniffing, mac flood, ARP poisoning, vulnerability of DNS, Kaminsky attack. TCP session hijecking, MitM attack, DoS and Distributed DoS, Route hijacking.
• Security planning : security plan content, risk analysis.
• Countermeasures
◦ Design principles of policies and mechanisms.
◦ Models: AAA, confinement, DAC, MAC, access control matrix 
◦ Cryptographic techniques:
▪ critptography basics (hash, symmetric c., asymmetric c., MAC, digital signature), birthday attack, rainbow, key quality, pseudo-random number generators.
▪ Authentication protocols and key exchange. replay and reflection attacks. Nonces. Perfect Forward Secrecy. Diffie-Helman. 
▪ Certificates, certification authority, public key infrastructures and their vulnerabilities.
▪ Applications: Protocols ssl, tls, ssh, virtual private networks, ipsec, etc. Autnetication protocols wan and lan. radius and vulnerabilities. Other applications.
◦ Anomaly detection systems.
◦ System security:
▪ general principles: passwords and their vulnerabilities, hardening, assessment and auditing
▪ unix: discertionaly access control, file system security, authentication, PAM, syslog
◦ Network security:
▪ Firewalling:stateless and statefull firewall, connections, syn-proxy and syn-cookies, load balancing and high availability, linux netfilter and configuration examples.
▪ Network siecurity at level 1 and 2.
▪ Applicative proxies and network intrusion detection systems .
• Authenticated Data Structures
• Distributed Ledger Technologies and Bitcoin
• Smart contracts
• Cybersecurity in big organizations.

Course handouts

The program is defined at the beginning of the course on the basis of the invited foreign professors. The website of the course (available on Moodle) is always kept up to date with the latest information.

Teaching materials and texts are chosen by foreign professors and will be published on the site of the course on Moodle.

Data and Visualization: Data overloading. Comparison of Scientific Visualization and Information Visualization. Structured and Unstructured data. Data transformation. Data tables.

Visual Perception: Our vision’s principles and limitations. Peripheral and central view. The perception of color.

Cognitive Issues and User Tasks: Perception abilities. Weber's law. Stevens' power law. Gestalt laws. A two stage model for visual perception. Task taxonomies.

Infovis on the Web - SVG and D3.js: Basic ingredients of Web data visualization. JavaScript crash course. Raster and vector graphics. Overview of JavaScript libraries. Focus on D3.js.

Multivariate Data Representations: Combined views. Icons or glyphs. Alternative coordinate systems.

Visualization in Computer Networks: Visual analysis in the computer network domain. Motivations. Taxonomies. Real-world examples and use cases. Open questions.

Design Methods and Evaluation: Design methodologies and design choices. Design evaluation (goals, difficulties, practices, guidelines).

Visualization of Time Series Data: Definition of time series and temporal data. Visualization of time series (single dependent variable, multiple dependent variables). Case studies.

Interaction: Classification of interaction mechanisms, goals, and timings. Examples of interaction strategies.

Introduction to Graph Drawing: Graph Drawing conventions and aesthetics. The divide an conquer approach for testing planarity of a graph.

Node-link Representations of Trees: Representing trees within the node-link paradigm. Layered drawings of trees. Hv-drawings of trees. Limitations of node-link representations.

Space-Filling Visualizations of Trees: Algorithms and systems for the representation of trees using the space-filling strategy. Treemaps. 3D Space-filling approaches.

Representations of Graphs and Networks with the Force-Directed Approach: The force-directed paradigm. The barycenter method. Spring embedders. Scalability and flexibility of the force-directed paradigm. Fruchterman-Reingold and Barnes–Hut algorithms. Simulating graph theoretic distances. Magnetic fields. Generic energy functions. Handling drawing constraints.

Representations of Hierarchical Data: Algorithms for the representation of layered networks. The Sugiyama approach. Step 1: Cycle removal. Step 2: Level Assignment. Step 3: Crossing Reduction. Step 4: X-Coordinate Assignment

Orthogonal Drawings: Computing orthogonal drawings via Network Flows. The Topology-Shape-Metric approach. Extension to graphs of arbitrary degree. Representations of orthogonal drawings obtained from visibility representations and by incremental approaches.

Visualizing Large Graphs: Strategies for the visualization of massive amount of data providing both overview and details. Alternate between views. Combine different views. Filtering and clustering principles. Three-dimensional and two-dimensional representations of clustered graphs. Hybrid representations.

Tools and Libraries for Drawing Graphs: Tools and Libraries for drawing graphs. Programming languages, input and output formats, and interaction. Some practical example.

Architectures for Scalable Information Visualization: Computational and memory scalabity. Visualization architectures. Strategies for visualizing massive amounts of data.

Slides provided by the teacher and downloadable day by day from the course website: http://www.dia.uniroma3.it/~infovis/
In order to download the slides a userid-password pair is necessary (ask the teacher at maurizio.patrignani@uniroma3.it)

Fundamentals: Software architecture concepts; Architectural descriptions; Software qualities; The architecture definition process.
Software qualities and designing for software qualities: performance; modifiability; availability; veriafiability; scalability; interoperability; monitoring.
Architectural patterns: Domain Model; Domain Object; Layers; Pipes & Filters; other POSA patterns; hexagonal architecture.
Architecture of distributed systems; client/server, peer-to-peer, remote invocation; broker; asynchronous communication; messaging; components; component-based architecture; container.
Service-based architecture; services; service-based architecture; REST services; microservices.
DevOps and software delivery: environments and their management; cloud computing; virtual machines and system virtualization; containers and container-based virtualization; container orchestration; continuous delivery.
Middleware.

Luca Cabibbo
Architettura del Software: Strutture e Qualità
Edizioni Efesto, 2021
ISBN: 9788833812458

Internet structure and the relationship between Internet, the Data Centers, and the Cloud
The Italian National Strategic Center (Polo Strategico Nazionale): A big Data Center in construction
The Italian Public Connectivity System (Sistema Pubblico di Connettività): A set of big private networks connected among themselves and with the Internet
Generalities on routing algorithms for the fixed network infrastructure
Level 3 algorithms and protocols
Distance Vector algorithms
Link-State-Packet algorithms
Routing protocols
Level 2 algorithms and protocols
Spanning tree algorithm for switched networks
VLAN: virtual local area networks
Evolution of the spanning tree protocol
Software Defined Networks
Interdomain routing
Border Gateway Protocol
BGP pitfalls
BGP scalability
Internet hierarchy
Using the RIPE Stat service
Using the AS Rank service
Using Looking Glasses - the case of Hurricane Electric
Internet anomalies
MPLS-based routing of an ISP
TCP and efficient transmission techinques
TCP efficiency for interactive services
TCP congestion control
TCP self-clocking and the bandwidth-delay product
TCP AIMD (Additive-Increase Multiplicative-Decrease) behaviour
BBR TCP
Routing in the data centers
Web-based services: from Data centers to CDNs
Architectures, models, and algorithms for Web-based services
Local distribution
Global distribution
Content delivery networks
IPv6 protocols
Address space and basic aspects of IPv6
The ICMPv6 protocol
Source address selection e multihoming
IPv4-IPv6 Transition mechanisms

Slides provided by the teacher and downloadable day by day from the course website: http://www.dia.uniroma3.it/~impianti/
In order to download the slides a userid-password pair is necessary (ask the teacher at maurizio.patrignani@uniroma3.it)

Fundamentals of data communications and networking
Circuit and packet switching, connection-oriented and connectionless services, network topologies and classification. Personal area networks (PAN), local area networks (LAN), metropolitan area networks (MAN), wide area networks (WAN), storage area networks (SAN). Network architectures and protocols.
Signal characteristics and Fourier analysis. Baseband and passband transmissions. Pulse code modulation (PCM), frequency division multiplexing (FDM), time division multiplexing (TDM), code division multiplexing (CDM), orthogonal frequency division multiplexing (OFDM).

Cable communication systems
Twisted pairs, coaxial cables and optical fibers. Digital subscriber lines (DSL). connectors. Laser diodes, LEDs and photodetectors. Synchronous digital hierarchy (SDH), synchronous optical networks (SONET). Fiber optic communication systems. Attenuation, dispersion, amplification in fiber optic systems. Wavelength division multiplexing (WDM). Optical transport networks (OTNs). Automatically switched transport networks (ASTN). Generalized multi-protocol label switching (GMPLS). Fiber optic access networks; passive optical networks (PON); fiber to the X (FTTX) systems. Fiber Channel.

Wireless communication systems
Propagation of electromagnetic waves. Fading. Optical wireless systems. Microwave transmission systems. Antennas for wireless communications. IEEE 802.11 (Wi-Fi). Bluetooth. Bluetooth Low Energy (BLE). Long Range WAN (LoRaWAN).

Mobile communications systems
Evolution of mobile systems, 4G and 5G systems.

Network security
Security Dimensions and levels. Symmetric and asymmetric cryptography. Data encryption standard (DES) and advanced encryption standard (AES). Wireless equivalent privacy (WEP) and Wi-Fi protected access (WPA). Quantum Key Distribution (QKD).

Andrew S. Tanenbaum - Computer Networks – 6 edizione
Stamatios V. Kartalopoulos - Next Generation Intelligent Optical Networks
Mahbub Hassan - Wireless and Mobile Networking

Decision making process. Introduction to Integer Linear Programming (ILP): relation between ILP and LP, equivalent formulations, relaxations, totally unimodular matrices, standard techniques for ILP modelling. ILP formulations: plant location, investment problem, sequencing problems, network optimization, transportation problems, set covering, set partitioning, set packing, crew scheduling. Exact algorithms: Branch and Bound, Cutting Planes, dynamic programming. Exact algorithms for binary and integer knapsack problems. Optimization on graphs: matching, vertex cover, max flow, independent set, Eulerian graphs and bipartite graphs. Use of an ILP commercial solver.

[1] M. FISCHETTI, "LEZIONI DI RICERCA OPERATIVA", EDIZIONI LIBRERIA PROGETTO PADOVA, ITALIA, 1995. (CAP. 2, 5, PARTE DEL 6 E DEL 7).
[2] R. AHUJA, T. MAGNANTI, J. ORLIN, "NETWORK FLOWS", PRENTICE HALL, 1993. (PG. 189-191, 473-475, 494-496)
[3] Lecture notes

Overview on decision making and Decision Support Systems (DSS). Model Driven DSS.
Mathematical modeling (examples of LP, ILP, and NLP formulations). Basics on computational complexity.
Introduction to Business Analytics. Predictive analytics, optimal classification trees, examples.
Prescriptive analytics. Heuristic algorithms: constructive heuristics, local search, variable depth local search, Tabu Search, Simulated Annealing, genetic algorithms, GRASP, Iterated Local Search, Variable Neighborhood Search, Guided Local Search, Ant Colony Optimization, PSO, Scatter Search, Path relinking...
Robust Optimization.
Study of real world cases (optimization of the flows in the distribution of frozen food, optimization of staff shifts in hospital departments, optimial routing for the collection of material for laboratory analysis, optimal management of the warehouse of a company that deals with online sales, ....).

1. Modelli e metodi decisionali in condizioni di incertezza e rischio, di G. Ghiani, R. Musmanno (a cura di), McGraw-Hill Education, 2009.
2. Slides e notes given by the lecturer

Database technology: physical structures, transaction management, distributed architectures. Data bases for decision support, database performance analysis and modeling. Seminars on recent topics in database management.

P. Atzeni et al. Basi di dati 5/Ed. McGraw-Hill, 2018 (or any major database textbook, contact the instructor for advice)
D. Menasce et al. Performance by Design: Computer Capacity Planning by Example, 2004
Additional material available on the course site:
http://www.dia.uniroma3.it/~atzeni/didattica/BD/BDIIindex.html

Database technology: physical structures, transaction management, distributed architectures. Data bases for decision support, data warehousing. Seminars on recent topics in database management.

P. Atzeni et al. Basi di dati 5/Ed. McGraw-Hill, 2018 (or any major database textbook, contact the instructor for advice)
Additional material available on the course site:
http://www.dia.uniroma3.it/~atzeni/didattica/BD/BDIIindex.html

Introduction
Human vision system
Spatial domain filtering
Transform domain filtering
Wavelet transform
Noise models
Image compression
Audio signal processing
Video compression
Haptic systems
Python

R.C. Gonzalez, R.E. Woods, and S. L. Eddins, "Digital Image Processing Using MATLAB, 2e", Publisher: Prentice-Hall;
B. Block and P. McNally, “3D storytelling: how stereoscopic 3D works and how to use it”, Publisher: Focal Press;
C. W. Chen, Z. Li and S. Lian, “Intelligent Multimedia Communication: Techniques and Applications (Studies in Computational Intelligence)”, Publisher: Springer;

Chapter and section numbers are taken from the basic textbook S. Ross "INTRODUCTION TO PROBABILITY AND STATISTICS FOR ENGINEERS AND SCIENTISTS"

Cap. 1: 1.1, 1.2, 1.3
Cap. 2: 2.2, 2.2.1, 2.2.2, 2.3, 2.3.1, 2.3.2, 2.4, 2.5, 2.6
Cap. 3: 3.1, 3.2, 3.3, 3.4, 3.5, 3.5.1, 3.6, 3.7, 3.8
Cap. 4: 4.1, 4.2, 4.3, 4.3.1, 4.3.2, 4.3.3, 4.4, 4.5, 4.5.1, 4.6, 4.7, 4.9
Cap. 5: 5.1, 5.2, 5.4, 5.5, 5.6, 5.6.1, 5.7, 5.8.1., 5.8.2.
Cap. 6: 6.1, 6.2, 6.3, 6.3.1, 6.3.2, 6.4, 6.5, 6.5.1, 6.5.2
Cap. 7: 7.1, 7.2, 7.3, 7.3.1, 7.3.2, 7.4, 7.5, 7.7
Cap. 8: 8.1, 8.2, 8.3, 8.3.1, 8.3.2, 8.4.1, 8.6,
Cap. 9: 9.1, 9.2, 9.3, 9.6
Cap. 11: 11.1, 11.2, 11.4 (for this part you may also look at chapters 6.4, 6.5 of the book (in Italian) "Moduli di matematica e statistica con uso di R" di S. Invernizzi, M. Rinaldi e F. Comoglio).

- Sheldon Ross NTRODUCTION TO PROBABILITY AND STATISTICS FOR ENGINEERS AND SCIENTISTS
- "Moduli di matematica e statistica" di S. Invernizzi, M. Rinaldi e F. Comoglio, ed. Zanichelli.

additional reference: Luca Leuzzi, Enzo Marinari, Giorgio Parisi
CALCOLO DELLE PROBABILITÀ: un trattatello per principianti volenterosi

1. Algorithms and artificial intelligence

- Introductory notes on algorithms and problem solving from an engineering point of view
- Evolutionary Calculation: Genetic Algorithms, Swarm Intelligence.
- The Neural Networks.
- Programming languages ​​and development environments: python.

2. Application of artificial intelligence in industrial and information engineering

- Application of Evolutionary Calculus for optimization and inverse problems in materials engineering, energy and biomedical fields.
- Neural, genetic and swarm intelligence models applied to the modeling of materials and devices.
- Application of AI and machine learning in design.

3. Applications of AI to music and art in general

- ML techniques for musical composition. How to computerize the musical signal: MIDI.
- Generative artificial intelligence in the visual and literary arts.

Zhng, A., Lipton, Z. C., Li, M., & Smola, A. J. (2021). Dive into deep learning. arXiv preprint arXiv:2106.11342. Available online: https://d2l.ai/

1. Algorithms and Artificial Intelligence

- Introductory notes on algorithms and problem-solving from an engineering perspective.
- Evolutionary computation: Genetic Algorithms, Swarm Intelligence.
- Neural Networks.
- Programming languages and development environments: Python.

2. Application of Artificial Intelligence in Industrial and Information Engineering

- Application of Evolutionary Computation for optimization and inverse problems in materials engineering, energy, and biomedical fields.
- Neural, genetic, and swarm intelligence models applied to materials and devices modeling.
- Application of AI and machine learning in the field of design process.

3. Applications of AI in Music and Art in General

- Machine Learning techniques for musical composition. Digitizing musical signals: MIDI.
- Generative artificial intelligence in visual and literary arts.

Zhng, A., Lipton, Z. C., Li, M., & Smola, A. J. (2021). "Dive into Deep Learning." arXiv preprint arXiv:2106.11342. Available online: https://d2l.ai/

"Artificial Intelligence and Art" - ISBN-13 978-8816606012 Publisher: Jaca Book Publication Date: November 5, 2020

Lecture notes provided by the professor.

- Introduction to CPS: applications and evolution
- Modeling CPS
- Communication standards and technologies for CPS
- Real time system
- Fault diagnosis: model based detection-isolation-identification of anomalies
- Model based anomaly detection for cyber attacks

Notes of the course
Scientific papers

Source discovery
Data quality
Data integration

Slides by the teacher

• Course introduction
• Introduction to computer security and terminology
• Vulneability and threats
◦ Software vulnerabilities. Trusted and untrusted input, input validation. Vulnerabilities of applications written with interpreted languages, code injection. Injection into web pages: XSS. Cross site request forgery. OWASP.
▪ Example of web site that is vulnerable to sql injection
◦ buffer overflow attacks. Exploitation: privilege excalation, intrusions through opens services, intrusions through untrusted documents (email, web, etc).
▪ Example of vulnerable code, buffer overflow and related exploit
◦ Vulnerabilities of networks: sniffing, mac flood, ARP poisoning, vulnerability of DNS, Kaminsky attack. TCP session hijecking, MitM attack, DoS and Distributed DoS, Route hijacking.
• Security planning : security plan content, risk analysis.
• Countermeasures
◦ Design principles of policies and mechanisms.
◦ Models: AAA, confinement, DAC, MAC, access control matrix 
◦ Cryptographic techniques:
▪ critptography basics (hash, symmetric c., asymmetric c., MAC, digital signature), birthday attack, rainbow, key quality, pseudo-random number generators.
▪ Authentication protocols and key exchange. replay and reflection attacks. Nonces. Perfect Forward Secrecy. Diffie-Helman. 
▪ Certificates, certification authority, public key infrastructures and their vulnerabilities.
▪ Applications: Protocols ssl, tls, ssh, virtual private networks, ipsec, etc. Autnetication protocols wan and lan. radius and vulnerabilities. Other applications.
◦ Anomaly detection systems.
◦ System security:
▪ general principles: passwords and their vulnerabilities, hardening, assessment and auditing
▪ unix: discertionaly access control, file system security, authentication, PAM, syslog
◦ Network security:
▪ Firewalling:stateless and statefull firewall, connections, syn-proxy and syn-cookies, load balancing and high availability, linux netfilter and configuration examples.
▪ Network siecurity at level 1 and 2.
▪ Applicative proxies and network intrusion detection systems .
• Authenticated Data Structures
• Distributed Ledger Technologies and Bitcoin
• Smart contracts
• Cybersecurity in big organizations.

Course handouts

Decision making process. Introduction to Integer Linear Programming (ILP): relation between ILP and LP, equivalent formulations, relaxations, totally unimodular matrices, standard techniques for ILP modelling. ILP formulations: plant location, investment problem, sequencing problems, network optimization, transportation problems, set covering, set partitioning, set packing, crew scheduling. Exact algorithms: Branch and Bound, Cutting Planes, dynamic programming. Exact algorithms for binary and integer knapsack problems. Optimization on graphs: matching, vertex cover, max flow, independent set, Eulerian graphs and bipartite graphs. Use of an ILP commercial solver.

[1] M. FISCHETTI, "LEZIONI DI RICERCA OPERATIVA", EDIZIONI LIBRERIA PROGETTO PADOVA, ITALIA, 1995. (CAP. 2, 5, PARTE DEL 6 E DEL 7).
[2] R. AHUJA, T. MAGNANTI, J. ORLIN, "NETWORK FLOWS", PRENTICE HALL, 1993. (PG. 189-191, 473-475, 494-496)
[3] Lecture notes

Overview on decision making and Decision Support Systems (DSS). Model Driven DSS.
Mathematical modeling (examples of LP, ILP, and NLP formulations). Basics on computational complexity.
Introduction to Business Analytics. Predictive analytics, optimal classification trees, examples.
Prescriptive analytics. Heuristic algorithms: constructive heuristics, local search, variable depth local search, Tabu Search, Simulated Annealing, genetic algorithms, GRASP, Iterated Local Search, Variable Neighborhood Search, Guided Local Search, Ant Colony Optimization, PSO, Scatter Search, Path relinking...
Robust Optimization.
Study of real world cases (optimization of the flows in the distribution of frozen food, optimization of staff shifts in hospital departments, optimial routing for the collection of material for laboratory analysis, optimal management of the warehouse of a company that deals with online sales, ....).

1. Modelli e metodi decisionali in condizioni di incertezza e rischio, di G. Ghiani, R. Musmanno (a cura di), McGraw-Hill Education, 2009.
2. Slides e notes given by the lecturer

Fundamentals of data communications and networking
Circuit and packet switching, connection-oriented and connectionless services, network topologies and classification. Personal area networks (PAN), local area networks (LAN), metropolitan area networks (MAN), wide area networks (WAN), storage area networks (SAN). Network architectures and protocols.
Signal characteristics and Fourier analysis. Baseband and passband transmissions. Pulse code modulation (PCM), frequency division multiplexing (FDM), time division multiplexing (TDM), code division multiplexing (CDM), orthogonal frequency division multiplexing (OFDM).

Cable communication systems
Twisted pairs, coaxial cables and optical fibers. Digital subscriber lines (DSL). connectors. Laser diodes, LEDs and photodetectors. Synchronous digital hierarchy (SDH), synchronous optical networks (SONET). Fiber optic communication systems. Attenuation, dispersion, amplification in fiber optic systems. Wavelength division multiplexing (WDM). Optical transport networks (OTNs). Automatically switched transport networks (ASTN). Generalized multi-protocol label switching (GMPLS). Fiber optic access networks; passive optical networks (PON); fiber to the X (FTTX) systems. Fiber Channel.

Wireless communication systems
Propagation of electromagnetic waves. Fading. Optical wireless systems. Microwave transmission systems. Antennas for wireless communications. IEEE 802.11 (Wi-Fi). Bluetooth. Bluetooth Low Energy (BLE). Long Range WAN (LoRaWAN).

Mobile communications systems
Evolution of mobile systems, 4G and 5G systems.

Network security
Security Dimensions and levels. Symmetric and asymmetric cryptography. Data encryption standard (DES) and advanced encryption standard (AES). Wireless equivalent privacy (WEP) and Wi-Fi protected access (WPA). Quantum Key Distribution (QKD).

Andrew S. Tanenbaum - Computer Networks – 6 edizione
Stamatios V. Kartalopoulos - Next Generation Intelligent Optical Networks
Mahbub Hassan - Wireless and Mobile Networking

The program is defined at the beginning of the course on the basis of the invited foreign professors. The website of the course (available on Moodle) is always kept up to date with the latest information.

Teaching materials and texts are chosen by foreign professors and will be published on the site of the course on Moodle.

- Infrastructures and programming paradigms for big data
- The Hadoop Ecosystem
- Cloud computing
- Big data processing (MapReduce, Hive, Spark)
- NoSQL systems
- Big data analytics
- Data lakes
- Systems and applications
- Business seminars

Martin J. Fowler, PramodkumarJ. Sadalage. "NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence".
Teacher slides (available on the Web side of the course on Moodle)

1. Algorithms and artificial intelligence

- Introductory notes on algorithms and problem solving from an engineering point of view
- Evolutionary Calculation: Genetic Algorithms, Swarm Intelligence.
- The Neural Networks.
- Programming languages ​​and development environments: python.

2. Application of artificial intelligence in industrial and information engineering

- Application of Evolutionary Calculus for optimization and inverse problems in materials engineering, energy and biomedical fields.
- Neural, genetic and swarm intelligence models applied to the modeling of materials and devices.
- Application of AI and machine learning in design.

3. Applications of AI to music and art in general

- ML techniques for musical composition. How to computerize the musical signal: MIDI.
- Generative artificial intelligence in the visual and literary arts.

Zhng, A., Lipton, Z. C., Li, M., & Smola, A. J. (2021). Dive into deep learning. arXiv preprint arXiv:2106.11342. Available online: https://d2l.ai/

1. Algorithms and Artificial Intelligence

- Introductory notes on algorithms and problem-solving from an engineering perspective.
- Evolutionary computation: Genetic Algorithms, Swarm Intelligence.
- Neural Networks.
- Programming languages and development environments: Python.

2. Application of Artificial Intelligence in Industrial and Information Engineering

- Application of Evolutionary Computation for optimization and inverse problems in materials engineering, energy, and biomedical fields.
- Neural, genetic, and swarm intelligence models applied to materials and devices modeling.
- Application of AI and machine learning in the field of design process.

3. Applications of AI in Music and Art in General

- Machine Learning techniques for musical composition. Digitizing musical signals: MIDI.
- Generative artificial intelligence in visual and literary arts.

Zhng, A., Lipton, Z. C., Li, M., & Smola, A. J. (2021). "Dive into Deep Learning." arXiv preprint arXiv:2106.11342. Available online: https://d2l.ai/

"Artificial Intelligence and Art" - ISBN-13 978-8816606012 Publisher: Jaca Book Publication Date: November 5, 2020

Lecture notes provided by the professor.

Introduction
Human vision system
Spatial domain filtering
Transform domain filtering
Wavelet transform
Noise models
Image compression
Audio signal processing
Video compression
Haptic systems
Python

R.C. Gonzalez, R.E. Woods, and S. L. Eddins, "Digital Image Processing Using MATLAB, 2e", Publisher: Prentice-Hall;
B. Block and P. McNally, “3D storytelling: how stereoscopic 3D works and how to use it”, Publisher: Focal Press;
C. W. Chen, Z. Li and S. Lian, “Intelligent Multimedia Communication: Techniques and Applications (Studies in Computational Intelligence)”, Publisher: Springer;

Decision making process. Introduction to Integer Linear Programming (ILP): relation between ILP and LP, equivalent formulations, relaxations, totally unimodular matrices, standard techniques for ILP modelling. ILP formulations: plant location, investment problem, sequencing problems, network optimization, transportation problems, set covering, set partitioning, set packing, crew scheduling. Exact algorithms: Branch and Bound, Cutting Planes, dynamic programming. Exact algorithms for binary and integer knapsack problems. Optimization on graphs: matching, vertex cover, max flow, independent set, Eulerian graphs and bipartite graphs. Use of an ILP commercial solver.

[1] M. FISCHETTI, "LEZIONI DI RICERCA OPERATIVA", EDIZIONI LIBRERIA PROGETTO PADOVA, ITALIA, 1995. (CAP. 2, 5, PARTE DEL 6 E DEL 7).
[2] R. AHUJA, T. MAGNANTI, J. ORLIN, "NETWORK FLOWS", PRENTICE HALL, 1993. (PG. 189-191, 473-475, 494-496)
[3] Lecture notes

Overview on decision making and Decision Support Systems (DSS). Model Driven DSS.
Mathematical modeling (examples of LP, ILP, and NLP formulations). Basics on computational complexity.
Introduction to Business Analytics. Predictive analytics, optimal classification trees, examples.
Prescriptive analytics. Heuristic algorithms: constructive heuristics, local search, variable depth local search, Tabu Search, Simulated Annealing, genetic algorithms, GRASP, Iterated Local Search, Variable Neighborhood Search, Guided Local Search, Ant Colony Optimization, PSO, Scatter Search, Path relinking...
Robust Optimization.
Study of real world cases (optimization of the flows in the distribution of frozen food, optimization of staff shifts in hospital departments, optimial routing for the collection of material for laboratory analysis, optimal management of the warehouse of a company that deals with online sales, ....).

1. Modelli e metodi decisionali in condizioni di incertezza e rischio, di G. Ghiani, R. Musmanno (a cura di), McGraw-Hill Education, 2009.
2. Slides e notes given by the lecturer

Fundamentals of data communications and networking
Circuit and packet switching, connection-oriented and connectionless services, network topologies and classification. Personal area networks (PAN), local area networks (LAN), metropolitan area networks (MAN), wide area networks (WAN), storage area networks (SAN). Network architectures and protocols.
Signal characteristics and Fourier analysis. Baseband and passband transmissions. Pulse code modulation (PCM), frequency division multiplexing (FDM), time division multiplexing (TDM), code division multiplexing (CDM), orthogonal frequency division multiplexing (OFDM).

Cable communication systems
Twisted pairs, coaxial cables and optical fibers. Digital subscriber lines (DSL). connectors. Laser diodes, LEDs and photodetectors. Synchronous digital hierarchy (SDH), synchronous optical networks (SONET). Fiber optic communication systems. Attenuation, dispersion, amplification in fiber optic systems. Wavelength division multiplexing (WDM). Optical transport networks (OTNs). Automatically switched transport networks (ASTN). Generalized multi-protocol label switching (GMPLS). Fiber optic access networks; passive optical networks (PON); fiber to the X (FTTX) systems. Fiber Channel.

Wireless communication systems
Propagation of electromagnetic waves. Fading. Optical wireless systems. Microwave transmission systems. Antennas for wireless communications. IEEE 802.11 (Wi-Fi). Bluetooth. Bluetooth Low Energy (BLE). Long Range WAN (LoRaWAN).

Mobile communications systems
Evolution of mobile systems, 4G and 5G systems.

Network security
Security Dimensions and levels. Symmetric and asymmetric cryptography. Data encryption standard (DES) and advanced encryption standard (AES). Wireless equivalent privacy (WEP) and Wi-Fi protected access (WPA). Quantum Key Distribution (QKD).

Andrew S. Tanenbaum - Computer Networks – 6 edizione
Stamatios V. Kartalopoulos - Next Generation Intelligent Optical Networks
Mahbub Hassan - Wireless and Mobile Networking

Fundamentals: Software architecture concepts; Architectural descriptions; Software qualities; The architecture definition process.
Software qualities and designing for software qualities: performance; modifiability; availability; veriafiability; scalability; interoperability; monitoring.
Architectural patterns: Domain Model; Domain Object; Layers; Pipes & Filters; other POSA patterns; hexagonal architecture.
Architecture of distributed systems; client/server, peer-to-peer, remote invocation; broker; asynchronous communication; messaging; components; component-based architecture; container.
Service-based architecture; services; service-based architecture; REST services; microservices.
DevOps and software delivery: environments and their management; cloud computing; virtual machines and system virtualization; containers and container-based virtualization; container orchestration; continuous delivery.
Middleware.

Luca Cabibbo
Architettura del Software: Strutture e Qualità
Edizioni Efesto, 2021
ISBN: 9788833812458

Lecture notes by the professor.

Chapter and section numbers are taken from the basic textbook S. Ross "INTRODUCTION TO PROBABILITY AND STATISTICS FOR ENGINEERS AND SCIENTISTS"

Cap. 1: 1.1, 1.2, 1.3
Cap. 2: 2.2, 2.2.1, 2.2.2, 2.3, 2.3.1, 2.3.2, 2.4, 2.5, 2.6
Cap. 3: 3.1, 3.2, 3.3, 3.4, 3.5, 3.5.1, 3.6, 3.7, 3.8
Cap. 4: 4.1, 4.2, 4.3, 4.3.1, 4.3.2, 4.3.3, 4.4, 4.5, 4.5.1, 4.6, 4.7, 4.9
Cap. 5: 5.1, 5.2, 5.4, 5.5, 5.6, 5.6.1, 5.7, 5.8.1., 5.8.2.
Cap. 6: 6.1, 6.2, 6.3, 6.3.1, 6.3.2, 6.4, 6.5, 6.5.1, 6.5.2
Cap. 7: 7.1, 7.2, 7.3, 7.3.1, 7.3.2, 7.4, 7.5, 7.7
Cap. 8: 8.1, 8.2, 8.3, 8.3.1, 8.3.2, 8.4.1, 8.6,
Cap. 9: 9.1, 9.2, 9.3, 9.6
Cap. 11: 11.1, 11.2, 11.4 (for this part you may also look at chapters 6.4, 6.5 of the book (in Italian) "Moduli di matematica e statistica con uso di R" di S. Invernizzi, M. Rinaldi e F. Comoglio).

- Sheldon Ross NTRODUCTION TO PROBABILITY AND STATISTICS FOR ENGINEERS AND SCIENTISTS
- "Moduli di matematica e statistica" di S. Invernizzi, M. Rinaldi e F. Comoglio, ed. Zanichelli.

additional reference: Luca Leuzzi, Enzo Marinari, Giorgio Parisi
CALCOLO DELLE PROBABILITÀ: un trattatello per principianti volenterosi

Hardware architectures; Software architectures; OpenGL, Vulkan; CUDA and OpenCL; Curves, surfaces, volumes; Algorithms for computer graphics and animation; Base concepts of physics-based animation.

Official documentations
- Unreal Game Engine:
- Vulkan
- DirectX
- OpenGL
- CUDA, OpenCL

• Course introduction
• Introduction to computer security and terminology
• Vulneability and threats
◦ Software vulnerabilities. Trusted and untrusted input, input validation. Vulnerabilities of applications written with interpreted languages, code injection. Injection into web pages: XSS. Cross site request forgery. OWASP.
▪ Example of web site that is vulnerable to sql injection
◦ buffer overflow attacks. Exploitation: privilege excalation, intrusions through opens services, intrusions through untrusted documents (email, web, etc).
▪ Example of vulnerable code, buffer overflow and related exploit
◦ Vulnerabilities of networks: sniffing, mac flood, ARP poisoning, vulnerability of DNS, Kaminsky attack. TCP session hijecking, MitM attack, DoS and Distributed DoS, Route hijacking.
• Security planning : security plan content, risk analysis.
• Countermeasures
◦ Design principles of policies and mechanisms.
◦ Models: AAA, confinement, DAC, MAC, access control matrix 
◦ Cryptographic techniques:
▪ critptography basics (hash, symmetric c., asymmetric c., MAC, digital signature), birthday attack, rainbow, key quality, pseudo-random number generators.
▪ Authentication protocols and key exchange. replay and reflection attacks. Nonces. Perfect Forward Secrecy. Diffie-Helman. 
▪ Certificates, certification authority, public key infrastructures and their vulnerabilities.
▪ Applications: Protocols ssl, tls, ssh, virtual private networks, ipsec, etc. Autnetication protocols wan and lan. radius and vulnerabilities. Other applications.
◦ Anomaly detection systems.
◦ System security:
▪ general principles: passwords and their vulnerabilities, hardening, assessment and auditing
▪ unix: discertionaly access control, file system security, authentication, PAM, syslog
◦ Network security:
▪ Firewalling:stateless and statefull firewall, connections, syn-proxy and syn-cookies, load balancing and high availability, linux netfilter and configuration examples.
▪ Network siecurity at level 1 and 2.
▪ Applicative proxies and network intrusion detection systems .
• Authenticated Data Structures
• Distributed Ledger Technologies and Bitcoin
• Smart contracts
• Cybersecurity in big organizations.

Course handouts

Introduction to DL; Training of Deep Architecture: hyperparameter tuning, batch normalization, faster optimizers, regularization ; Convolutional Neural Networks (CNN/ConvNets); Recurrent Neural Networks (GRU, LSTM, Bidirectional); Encoder-Decoder, Autoencoders, Variational Autoencoders; Attention layers ; Generative Adversarial Networks (GAN); Deep Reinforcement Learning; Embeddings; Use cases: AlexNet, VGG, NiN, GoogLeNet/Inception, ResNet, DenseNet; Use cases: Computer Vision and NLP

Simon J.D. Prince. "Understanding Deep Learning". MIT Press Dec 5th 2023 A. Geron,
“Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems”, O'Reilly Media, Inc, USA, 2019.

Source discovery
Data quality
Data integration

Slides by the teacher

The program is defined at the beginning of the course on the basis of the invited foreign professors. The website of the course (available on Moodle) is always kept up to date with the latest information.

Teaching materials and texts are chosen by foreign professors and will be published on the site of the course on Moodle.

The course will examine various methods for designing, implementing, and testing adaptive systems on the Web, realized through Artificial Intelligence techniques. We will pay particular attention to Information Retrieval systems, such as search engines, and new and emerging technologies suitable for developing the next generation of intelligent and personalized search tools. We will study classical retrieval models, such as the vector space and probabilistic models, document ranking techniques, as well as the PageRank algorithm adopted by Google. Algorithms of Machine Learning in Information Retrieval will be addressed, including Sentiment Analysis techniques, User Modeling methods needed for developing personalized research tools, Recommender Systems, and the detection and analysis of Online Communities and Social Networks (such as Facebook and Twitter).

Lectures will cover topics dealt with in scientific papers and reference texts.
The teacher will make slides from the lectures available through the course website. Those slides will be self-contained, that is, written in such a way as not to require the consultation of other material for passing the exam.

Data and Visualization: Data overloading. Comparison of Scientific Visualization and Information Visualization. Structured and Unstructured data. Data transformation. Data tables.

Visual Perception: Our vision’s principles and limitations. Peripheral and central view. The perception of color.

Cognitive Issues and User Tasks: Perception abilities. Weber's law. Stevens' power law. Gestalt laws. A two stage model for visual perception. Task taxonomies.

Infovis on the Web - SVG and D3.js: Basic ingredients of Web data visualization. JavaScript crash course. Raster and vector graphics. Overview of JavaScript libraries. Focus on D3.js.

Multivariate Data Representations: Combined views. Icons or glyphs. Alternative coordinate systems.

Visualization in Computer Networks: Visual analysis in the computer network domain. Motivations. Taxonomies. Real-world examples and use cases. Open questions.

Design Methods and Evaluation: Design methodologies and design choices. Design evaluation (goals, difficulties, practices, guidelines).

Visualization of Time Series Data: Definition of time series and temporal data. Visualization of time series (single dependent variable, multiple dependent variables). Case studies.

Interaction: Classification of interaction mechanisms, goals, and timings. Examples of interaction strategies.

Introduction to Graph Drawing: Graph Drawing conventions and aesthetics. The divide an conquer approach for testing planarity of a graph.

Node-link Representations of Trees: Representing trees within the node-link paradigm. Layered drawings of trees. Hv-drawings of trees. Limitations of node-link representations.

Space-Filling Visualizations of Trees: Algorithms and systems for the representation of trees using the space-filling strategy. Treemaps. 3D Space-filling approaches.

Representations of Graphs and Networks with the Force-Directed Approach: The force-directed paradigm. The barycenter method. Spring embedders. Scalability and flexibility of the force-directed paradigm. Fruchterman-Reingold and Barnes–Hut algorithms. Simulating graph theoretic distances. Magnetic fields. Generic energy functions. Handling drawing constraints.

Representations of Hierarchical Data: Algorithms for the representation of layered networks. The Sugiyama approach. Step 1: Cycle removal. Step 2: Level Assignment. Step 3: Crossing Reduction. Step 4: X-Coordinate Assignment

Orthogonal Drawings: Computing orthogonal drawings via Network Flows. The Topology-Shape-Metric approach. Extension to graphs of arbitrary degree. Representations of orthogonal drawings obtained from visibility representations and by incremental approaches.

Visualizing Large Graphs: Strategies for the visualization of massive amount of data providing both overview and details. Alternate between views. Combine different views. Filtering and clustering principles. Three-dimensional and two-dimensional representations of clustered graphs. Hybrid representations.

Tools and Libraries for Drawing Graphs: Tools and Libraries for drawing graphs. Programming languages, input and output formats, and interaction. Some practical example.

Architectures for Scalable Information Visualization: Computational and memory scalabity. Visualization architectures. Strategies for visualizing massive amounts of data.

Slides provided by the teacher and downloadable day by day from the course website: http://www.dia.uniroma3.it/~infovis/
In order to download the slides a userid-password pair is necessary (ask the teacher at maurizio.patrignani@uniroma3.it)