The course introduces the fundamental concepts and results of probability theory, which are then applied in the fields of information theory and statistical inference.
Learning how to use the fundamental concepts of Probability Theory to acquire the main ideas of Information Theory, with particular reference to coding theory, and of inference based on the maximum likelihood principle, Bayes' theorem, and Markov chains.
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.
Basic notions of calculus.
Traditional.
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.
Notes prepared by the instructor.
Ricevimento: Appointment by email
LORENZO ROSASCO (President)
NICOLETTA NOCETI
ALESSANDRO VERRI (President Substitute)
According to the calendar approved by the Degree Program Board: https://corsi.unige.it/en/corsi/8759/studenti-orario
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
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.
For further information, please refer to the course’s AulaWeb module or contact the instructor.
