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B8310 - STATISTICS AND MACHINE LEARNING FOR SIGNAL PROCESSING M

Academic Year 2025/2026

                
                        Docente:
                        Mauro Mangia
                    
                        Credits:
                        6
                    
                        SSD:
                        ING-INF/01
                    
                        Language:
                        English
                    
                        Moduli:
                        
                            Mauro Mangia
                            (Modulo 1)
                        
                            Mauro Mangia
                            (Modulo 2)
                        
                        Teaching Mode:
                        In-person learning (entirely or partially)
                        
                                    In-person learning (entirely or partially) (Modulo 1); 
                                
                                    In-person learning (entirely or partially) (Modulo 2)
                                
                            Campus:
                            Bologna
                        
                            Corso:
                            Second cycle degree programme (LM) in
                            Electronic Engineering for Intelligent Vehicles (cod. 5917)

                            Teaching resources on Virtuale
                        
                                        Course Timetable
                                    
from Feb 17, 2026 to May 26, 2026

                                        Course Timetable
                                    
from Feb 16, 2026 to May 25, 2026

Learning outcomes

The course aims at reviewing basic concepts of probability, operator theory and optimization and using them in the development of fundamental signal processing methods ranging from filtering to spectrum estimation, linear prediction, adaptive sampling and dimensionality reduction.

Course contents

The course is organized into two complementary moduls.

Discrete-Time Signals and Systems

Discrete-time signals: representation and basic operations
Linear Time-Invariant (LTI) systems: impulse response, convolution, stability and causality
Discrete-Time Fourier Transform (DTFT): definition and properties
Z-transform: definition, region of convergence (ROC), system analysis
Digital filters: FIR and IIR structures, frequency response, design criteria
Sampling and quantization: quantization effects, quantization noise
Discrete Fourier Transform (DFT) and Fast Fourier Transform (FFT): principles and efficient implementation

Stochastic Processes and Modeling

Review of probability theory
Vector spaces and linear algebra
Optimization and optimization algorithms
Covariance and independence
Stochastic processes: properties, stationarity, ergodicity
Processing blocks and linear representations
Gaussian vectors and processes
Power Spectral Density (PSD)
Linear prediction, autoregressive models, and Yule-Walker equations
Wold theorem

The course provides theoretical and methodological tools for the analysis and design of deterministic and stochastic signal processing systems.

Teaching methods

Lectures for the presentation of theoretical concepts

Assessment methods

written exam

Office hours

See the website of Mauro Mangia