Università di Pavia - Offerta formativa

ARTIFICIAL INTELLIGENCE

Anno immatricolazione

2019/2020

Anno offerta

2019/2020

Normativa

DM270

SSD

ING-INF/05 (SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI)

Dipartimento

DIPARTIMENTO DI INGEGNERIA INDUSTRIALE E DELL'INFORMAZIONE

Corso di studio

COMPUTER ENGINEERING

Curriculum

Embedded and Control Systems

Anno di corso

1°

Periodo didattico

Primo Semestre (30/09/2019 - 20/01/2020)

Crediti

6

Ore

45 ore di attività frontale

Lingua insegnamento

English

Tipo esame

ORALE

Docente

PIASTRA MARCO (titolare) - 6 CFU

Prerequisiti

Basic mathematical skills, practical knowledge of at least one programming language.

Obiettivi formativi

The course follows a conceptual pathway along the fundamental principles of the discipline. It is divided into two parts: the first part is an introduction to classical formal logic, both propositional and first order, with a special focus to the aspects of automatic calculus, while the second part is an introduction to the basic principles of machine learning and self-organizing systems.

Programma e contenuti

Classical logic and automated symbolic reasoning

Boolean algebras

Logical language and semantical structures: logical consequence

Deductive systems for propositional logic

Decision problems and decidability

Predicates and relations: first order logic

Semi-decidability of first order logic

First-order resolution with unification

Machine Learning

Logic and probability: representation or statistics?

The language of probability: representation

Bayesian inference

Graphical models and automation

Probabilistic learning

Clustering: K-means and related methods

Self-organizing systems and applications

Boolean algebras

Logical language and semantical structures: logical consequence

Deductive systems for propositional logic

Decision problems and decidability

Predicates and relations: first order logic

Semi-decidability of first order logic

First-order resolution with unification

Machine Learning

Logic and probability: representation or statistics?

The language of probability: representation

Bayesian inference

Graphical models and automation

Probabilistic learning

Clustering: K-means and related methods

Self-organizing systems and applications

Metodi didattici

Lectures (hours/year in lecture theatre): 45

Practical class (hours/year in lecture theatre): 0

Practicals / Workshops (hours/year in lecture theatre): 0

Practical class (hours/year in lecture theatre): 0

Practicals / Workshops (hours/year in lecture theatre): 0

Testi di riferimento

See the home page of the course for lecture slides, suggested readings and software for the exercises

Modalità verifica apprendimento

The final exam is an interview about the theory, together with the discussion of practical activities in the lab.

Altre informazioni

The final exam is an interview about the theory, together with the discussion of practical activities in the lab.