Insegnamento

CFU

SSD

Ore Lezione

Ore Eserc.

Ore Lab

Ore Studio

Attività

Lingua

1047223 
NETWORKING FOR BIG DATA AND LABORATORY
(obiettivi)
These classes aim at providing the students with a comprehensive
understanding of networking principles and current networking technologies at an
introductory level. The focus in on the Internet evelution for big data support and the
cloud networking with special attention to networking solution for data centers. A
preliminary introductory part of the course is defined to equalize the background of
potentially heterogneoeus classes and to unify networking concepts, terms and technical
language.

CIANFRANI ANTONIO
( programma)
1. Basics
1a. Introduction to communication networks.
1b. Network and protocol architectures; transfer modes.
1c. TCP/IP networking (addressing, routing, transport).
1d. The Cloud Computing revolution.
2. Internet for big data and Cloud Networking
2a. Evolution of IP routing: from devicebased to locatorbased routing
(LISP protocol)
2b. Content Delivery Networks
2c. Software Defined Networking (SDN)
2d. Network Function Virtualization (NFV)
3. Data centers networking
3a. Architectures, examples. Topology design. Classic (three levels), fat tree, DCell, JellyFish.
3b. Data center traffic characterization.
3c. Reliability, energy and delay tradeoffs.
3d. Transport in data center networks. DCTCP.
3e. Distributed data center: scheduling and communication challenges.
4. Network performance analysis
4a. Performance metrics and Aims.
4b. Traffic description, characterization and measurements.
4c. Examples of simple dimensioning problems and related approaches.
4d. Simulation of service system for performance evaluation. Examples.
Lecture notes
(Date degli appelli d'esame)

9

INGINF/03

48



36



Attività formative caratterizzanti

ENG 
1047266 
STATISTICAL METHODS IN DATA SCIENCE AND LABORATORY
(obiettivi)
This course is a twosemester course aimed at providing the fundamental tools for
a) setting up probabilistic models b) understanding the basic principles of the main inferential problems: estimation, hypothesis testing, model checking and forecasting. c) implementing inference on observed data through the likelihood function using both optimization and simulationbased approximations (Bootstrap, Monte Carlo and Monte Carlo Markov Chian) d) understanding comparative merits of alternative approximation startegies e) developing statistical computations within a suitable software environment



STATISTICAL METHODS IN DATA SCIENCE AND LABORATORY II
(obiettivi)
This course is a twosemester course aimed at providing the fundamental tools for
a) setting up probabilistic models b) understanding the basic principles of the main inferential problems: estimation, hypothesis testing, model checking and forecasting. c) implementing inference on observed data through the likelihood function using both optimization and simulationbased approximations (Bootstrap, Monte Carlo and Monte Carlo Markov Chian) d) understanding comparative merits of alternative approximation startegies e) developing statistical computations within a suitable software environment

TARDELLA LUCA
( programma)
Approximate methods in statistics: overview of optimization and integration techniques for model fitting. Monte Carlo methods. Importance Sampling. Error control, Coincise introduction to Markov Chains on general state space and ergodic theory. Monte Carlo Markov Chain for Bayesian inference: MetropolisHastings and Gibbs Sampling. The EM algoritm and its generalizations. Parametric and nonparametric bootstrap, permutation tests.
S.M. Ross: Introduction to probability and statistics for engineers and scientists, 3rd ed. Elsevier.
Larry Wasserman. All of Statistics: A Concise Course in Statistical Inference. Springer, 2004
L. Chihara, T. Hesterberg: Mathematical Statistics with resampling and R. Wiley
F.M. Dekking, C. Kraaikamp, H.P. Lopuhaa, L.E. Meester: A Modern Introduction to Probability and Statistics. Springer.
Christian P. Robert and George Casella. Monte Carlo statistical methods (2nd ed) SpringerVerlag Inc, 2004.
Liu, Monte Carlo Methods in Scientific Computing, SpringerVerlag, 2001  Discuss SMC and also MCMC
Handbook of Markov Chain Monte Carlo  Steve Brooks, Andrew Gelman, Galin Jones, XiaoLi Meng Chapman and Hall/CRC, 2001
(Date degli appelli d'esame)

6

SECSS/01

40



12



Attività formative caratterizzanti

ENG 
Gruppo opzionale:
GRUPPO OPZIONALE B  (visualizza)

18








1047197 
DATA MANAGEMENT FOR DATA SCIENCE
(obiettivi)
The main goal of the course is to present the basic concepts of data
management systems. The first part of the course introduces the main aspects
of relational database systems, including basic functionalities, file and index
organizations, and query processing. The second part of the course aims
at presenting the main nonrelational approaches to data management, in
particular, multidimensional data management, largescale data management,
and open data management.

ROSATI RICCARDO
( programma)
1. The structure of a Data Base Management System
 Basic functionalities of data server
2. Physical structures for data
 file organizations, indexed organizations
3. Principles of query evaluation
 evaluation of relational algebra operators, query planning and optimization
4. Multidimensional data
 OLAP Queries, Structures for multidimensional data, OLAP query evaluation
5. Largescale data management
 Distributed query evaluation, NoSQL databases, graph databases
6. Open data management
 open data, linked open data, RDF databases
(Date degli appelli d'esame)

6

INGINF/05

48







Attività formative caratterizzanti

ENG 
1047205 
CLOUD COMPUTING
(obiettivi)
Cloud computing has entered the mainstream of information technology,
providing highly elastic scalability in delivery of enterprise applications. At the
end of the course students will have the tools to understand the impact of using
Cloud services in a business environment and the technological implications of
developing Cloud applications in practice, especially for storing and processing
large data sets.

6

INF/01

48







Attività formative caratterizzanti

ENG 
1047200 
DATA MINING TECHNOLOGY FOR BUSINESS AND SOCIETY
(obiettivi)
The course will present fundamental technologies for advanced data mining
applications. The course will start with presenting the methodologies for storing
and retrieving information on the Web, mining application logs, mining social
media, collaborative filtering and personalization. The course will also present
the basic technlogies for mining geospatial data and data produced from
largescale sensing environments. Applications will include mining of consumer
preferences, computational advertising, online marketplaces, digital marketing
and mining data in physical spaces. As part of the course students will carry on a
field study on a relevant use case for a selected application.

LEONARDI STEFANO
( programma)
○ Basic concepts of Web Information Retrieval and Search Engine Architecture.
○ Web Usage and Social Media Data Mining
○ Recommendation Systems and Personalization
○ Data Mining in Computational Advertising and Online Markets.
○ Digital marketing and customer preferences.
○ Online labour marketplaces and Collaborative systems.
○ Geospatial Data mining
○ Mining sensing environments
(Date degli appelli d'esame)

6

INGINF/05

48







Attività formative caratterizzanti

ENG 
1047213 
DATA MONITORING ANALYSIS AND COMMUNICATION
(obiettivi)
The course aims at giving instruments to understand the role of information and its
representation. How this information should be acquired, the istruments used as well as, the
possible compression methods will be a focus of this course. More, passing from theory to
practice, some specific problems of data monitoring related to vehicles and pollution will be
presented by showing also the impact of array processing. Last, some methods for transferring
information will be highlighted.

BIAGI MAURO
( programma)
a. Introduction (Historical notes, the role of information and monitoring, communication vs.
information)
b. Sensors (Transducers, Active sensors, Passive sensors)
c. Information Measurement (sampling: undersampling, oversampling, compressive
sensing, spectral analysis and estimation, information theory and reliability theory, quantum
information theory and quantum calculus, lossy and lossless coding)
d. Data Monitoring: (Underwater environment monitoring (experiences in Lab available),
Air pollution monitoring, Vehicle traffic analysis via smart lighting systems, Diversity
techniques )
e. Data Analysis and communications: quantum computers essentials, Intelligent
Transportation systems, Underwater Acoustic Communications (experiences in Lab
available), Visible Light Communications, Array processing
(Date degli appelli d'esame)

6

INGINF/03

48







Attività formative caratterizzanti

ENG 

Gruppo opzionale:
GRUPPO OPZIONALE C  (visualizza)

6








1047208 
STATISTICAL LEARNING
(obiettivi)
Devising new machine learning methods and statistical models is a fun and extremely fruitful “art”. But these powerful tools are not useful unless we understand when they work, and when they fail. The main goal of statistical learning theory is thus to study, in a statistical framework, the properties of learning algorithms, mainly in the form of socalled error bounds. This course introduces the techniques that are used to obtain such results, combining methodology with theoretical foundations and computational aspects. It treats both the basic principles to design successful learning algorithms and the “science” of analyzing an algorithm’s statistical properties and performance guarantees. Theorems are presented together with practical aspects of methodology and intuition to help students developing tools for selecting appropriate methods and approaches to problems in their own data analyses. Methods for a wide variety of applied problems will be explored and implemented on opensource software like R (www.rproject.org) and OpenBUGS (www.openbugs.info/w/).

BRUTTI PIERPAOLO
( programma)
1. Review of basic probability and inference. 2. Concentration of measure. 3. Basics of convex optimization. 4. Statistical functional: bootstrap & subsampling. 5. Nonparametric Regression and Density estimation: kernels and RKHS. 6. Nonparametric Classification. 7. Nonparametric Clustering: kmeans, density clustering. 8. Graphical Models and their applications: parametric and nonparametric approaches. 9. Hints of Nonparametric Bayes 10. Minimaxity & Sparsity Theory.
Riferimenti principali
Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani (2013). An Introduction to Statistical Learning with Applications in R. Available at: http://wwwbcf.usc.edu/~gareth/ISL/ Larry Wasserman (2005). All of Nonparametric Statistics. Stephen Boyd and Lieven Vandenberghe. Convex Optimization. Available at: http://stanford.edu/~boyd/cvxbook/
Ulteriori approfondimenti
Trevor Hastie, Robert Tibshirani and Jerome Friedman (2009). The Elements of Statistical Learning. Available at: http://statweb.stanford.edu/~tibs/ElemStatLearn/ Trevor Hastie, Robert Tibshirani e Martin Wainwright (2015). Statistical Learning with Sparsity: The Lasso and Generalizations. Christophe Giraud (2014). Introduction to HighDimensional Statistics. Chris Bishop (2006). Pattern Recognition and Machine Learning.
(Date degli appelli d'esame)

6

SECSS/01

48







Attività formative caratterizzanti

ENG 
1047209 
QUANTITATIVE MODELS FOR ECONOMIC ANALYSIS AND MANAGEMENT
(obiettivi)
○ Present a general overview on the economic background of the quantitative
models that will be presented during the course;
○ Provide the basic concepts to analyse the specialised literature;
○ Propose a unified framework on the main methodologies available to estimate
and compare productivity and efficiency of Decision Making Units (DMUs);
○ Make an introduction to the main softwares available to implement the
quantitative models presented during the course;
○ Provide laboratory sessions to implement productivity and efficiency analyses,
as well as the other quantitative models, in practice;
○ Present several applications in the field of economics and management,
including public sector services.
○ Interact with students through assisted laboratory, oral presentations and the
realization of a project work on real data at the end of the course.

DARAIO CINZIA
( programma)
○ The course is composed by the following 7 modules.
○ Introduction, basic concepts and notation.
○ Nature, collection, semantic modelling, and analysis of microdata within
organizations and socioeconomic systems.
○ Efficiency and productivity analysis: main techniques and applications.
○ Structural and interpretative models of discrete choices.
○ Classification and Recommendation Systems for the management of
organizations and socioeconomic systems
○ Economic analysis of consumer behaviour.
○ Applications in economics and management.
(Date degli appelli d'esame)

6

INGIND/35

48







Attività formative caratterizzanti

ENG 

 
A SCELTA DELLO STUDENTE

6


48







Attività formative a scelta dello studente (art.10, comma 5, lettera a)

ITA 