ID:
502522
Duration (hours):
56
CFU:
6
SSD:
AUTOMATICA
Year:
2025
Overview
Date/time interval
Annualità Singola (29/09/2025 - 12/06/2026)
Syllabus
Course Objectives
Knowledge of basic notions of probability and statistics. Ability to solve data analysis and estimation problems ranging from model formulation to the use of computer tools (Matlab) for parameter estimation and model simulation.
Course Prerequisites
Basic notions of set theory, logic, calculus, function maximization.
Teaching Methods
Teaching is based on lectures, tutorials and laboratory activities in the computer room.
For teaching activities, slide presentations are used, which are made available to students in the teaching section on the KIRO platform, where video recordings of lectures and teaching materials for exercises and labs are also available. During laboratory hours, students are introduced to the different phases of data analysis: import, visualisation, exploratory analysis, modelling. Attendance at laboratory activities is optional.
For teaching activities, slide presentations are used, which are made available to students in the teaching section on the KIRO platform, where video recordings of lectures and teaching materials for exercises and labs are also available. During laboratory hours, students are introduced to the different phases of data analysis: import, visualisation, exploratory analysis, modelling. Attendance at laboratory activities is optional.
Assessment Methods
Written examination: two questions of a theoretical nature and two of a practical nature. One of the theoretical questions may be a closed true/false answer. Duration is between 1.5 and 2 hours. The assessment, in thirtieths, is the average of the marks for the individual answers weighted according to their difficulty. Texts, textbooks and notes may not be consulted during the test. Carrying out the project is optional. During the lectures are presented examples of examination questions.
Texts
Lecture notes (http://sisdin.unipv.it/labsisdin/teaching/teaching.php).
M. Bramanti. Calcolo delle probabilità e statistica. Esculapio.
A. Papoulis. Probability, Random Variables, and Stochastic Processes. McGraw-Hill.
L. Ljung. System Identification: Theory for the User. Prentice-Hall.
M. Bramanti. Calcolo delle probabilità e statistica. Esculapio.
A. Papoulis. Probability, Random Variables, and Stochastic Processes. McGraw-Hill.
L. Ljung. System Identification: Theory for the User. Prentice-Hall.
Contents
System Identification deals with methodologies that enable the construction of mathematical models of systems and signals based on experimental data. In presence of complex systems whose behavior can be hardly reduced to known "laws of nature", the use of identification techniques is often the only way to obtain models to be used in the context of forecasting, simulation, and control. The methods presented in the course are widely used in heterogeneous fields such as automation, biomedical engineering, econometry, hydrology, geophysics and telecommunications. Some basic notions of probability, estimation theory and stochastic processes are recalled. The main properties (stability, input-output description in the time and frequenct domains) of linear discrete-time systems are introduced. In the context of parametric estimation, the issues of model validation and model complexity are extensively discussed. Neural based identification is also illustrated and discussed, pointing out pros and cons with respect to standard approaches. The study of dynamic systems addresses three main topics: the optimal prediction of stationary stochastic processes (Wiener filtering), the identification of linear discrete-time systems, and spectral estimation (both nonparametric and maximum-entropy).
Probability: basic notions
probability notion;
independence, conditional probability, total probability and Bayes theorems;
Bernoulli trials, Poisson events;
the notion of random variable (R.V.), cumulative distribution function, probability density function, functions on one R.V.;
mode, median, moments of a R.V.;
joint random variables: distribution, density, moments, independence, incorrelation, functions of random variables;
Law of Lrge Numbers, Gaussian R.V., Central Limit Theorem.
Statistics: basic notions
notion of estimator; properties of estimators;
sample moments and their main properties;
confidence interval for the sample mean, Student's t.
Identification of linear-in-parameter models:
the least squares method, normal equations, identifiability;
Best Linear Unbiased Estimator: estimator, variance of parameters;
validation and choice of complexity: chi-square test, F-test, FPE, AIC, and MDL criteria.
Probability: basic notions
probability notion;
independence, conditional probability, total probability and Bayes theorems;
Bernoulli trials, Poisson events;
the notion of random variable (R.V.), cumulative distribution function, probability density function, functions on one R.V.;
mode, median, moments of a R.V.;
joint random variables: distribution, density, moments, independence, incorrelation, functions of random variables;
Law of Lrge Numbers, Gaussian R.V., Central Limit Theorem.
Statistics: basic notions
notion of estimator; properties of estimators;
sample moments and their main properties;
confidence interval for the sample mean, Student's t.
Identification of linear-in-parameter models:
the least squares method, normal equations, identifiability;
Best Linear Unbiased Estimator: estimator, variance of parameters;
validation and choice of complexity: chi-square test, F-test, FPE, AIC, and MDL criteria.
Course Language
Italian
Degrees
Degrees
ELECTRONIC AND COMPUTER ENGINEERING
Bachelor’s Degree
3 years
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People
People
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