OVERVIEW

The course introduces the fundamental concepts and results of probability theory, which are then applied in the fields of information theory and statistical inference.

AIMS AND CONTENT

LEARNING OUTCOMES

The student will learn how to extract information and infer knowledge from data through the application of basic concepts and techniques from probability theory and statistics

AIMS AND LEARNING OUTCOMES

At the end of the course, students will have acquired fundamental concepts and results of probability and will be able to apply them to simple problems in information transmission and statistical inference.

PREREQUISITES

Basic notions of calculus.

TEACHING METHODS

Traditional.

SYLLABUS/CONTENT

The course introduces the basic concepts of probability theory and then explores the connections with the fundamentals of information theory and inference. About half of the course is covered through examples and exercises, including the implementation of some algorithms.

RECOMMENDED READING/BIBLIOGRAPHY

Notes prepared by the instructor.

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

According to the calendar approved by the Degree Program Board: https://corsi.unige.it/en/corsi/8759/studenti-orario

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

The exam consists of a written test and an optional oral test for those who wish to improve their written exam grade. During the course, some homework exercises will be assigned, similar to the exam tests.

Guidelines for students with certified Specific Learning Disorders, disabilities, or other special educational needs are available at https://corsi.unige.it/en/corsi/8759/studenti-disabilita-dsa 

ASSESSMENT METHODS

The written exam assesses the student's ability to apply the concepts learned in class by solving exercises similar to those covered during the course.

FURTHER INFORMATION

For further information, please refer to the course’s AulaWeb module or contact the instructor.