Information Theory

Learning Goals

Comprehensive view of subjects in information theory that are central to problems in statistical inference, advanced probability and probabilistic modeling (statistical mechanics). Working knowledge of the basic concepts in information theory and their application to statistical learning and mathematical modeling.

Program in pills

Asymptotic Equipartition Property and Shannon’s theorem. Entropy, mutual and relative information. Limit theorems for sums and extremes. Large deviation theory and distributions of maximal entropy. Elements of coding theory. Applications to statistical inference.

Area

Mathematical and statistical modelling

Curriculum Foundations
TAF Type

D

Curriculum Industry
TAF Type

D

Curriculum Health
TAF Type

D

Curriculum Economy
TAF Type

SSD

INF/01

ECTS

6

Semester

1

Lecturers

Matteo Marsili