ARTIFICIAL INTELLIGENCE
Stampa
Enrollment year
2021/2022
Academic year
2021/2022
Regulations
DM270
Academic discipline
ING-INF/05 (DATA PROCESSING SYSTEMS)
Department
DEPARTMENT OF PHYSICS
Course
Curriculum
Fisica delle tecnologie quantistiche
Year of study
Period
1st semester (04/10/2021 - 19/01/2022)
ECTS
6
Lesson hours
45 lesson hours
Language
English
Activity type
ORAL TEST
Teacher
PIASTRA MARCO (titolare) - 6 ECTS
Prerequisites

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

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 from a probabilistic perspective.
Course contents

1) 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

2) Machine Learning

Logic and probability: representation or statistics?
The language of probability: representation
Bayesian inference
Graphical models and automation
Probabilistic learning
Clustering: K-means, EM algorithm, missing data.
Causal models, probabilistic and structural.
Reinforcement Learning.
Teaching methods

Lectures (hours/year in lecture theatre): 4.5
Practical class (hours/year in lecture theatre): 0
Practicals / Workshops (hours/year in lecture theatre): 0
Reccomended or required readings

See the home page of the course (http://vision.unipv.it/AI) for lecture slides, suggested readings and software for the exercises.
Assessment methods

The final exam is an interview about the theory, together with the discussion of practical activities shown during lessons.
Further information
Sustainable development goals - Agenda 2030