|SCIENTIFIC DISCIPLINARY SECTOR||INF/01|
The course provides basic knowledge of classic and modern machine learning techniques, that can fruitfully be applied to disparate fields such as production line automation, quality monitoring, robotics, surveillance, self-driving vehicles, among many others.
This course provides an introduction to machine learning and statistical pattern recognition. Topics include: (1) Pattern recognition basics and theory. (2) Supervised learning ideas and methods. (3) Unsupervised learning ideas and some relevant methods. (4) Machine learning workflows and best practices. The course will also cover relevant success stories, and possible applications and case studies in the fields of robotics and smart industrial automation
After successfully attending the course, the student will be able to:
More basic topics (elements of probability, of statistics, of optimisation) will be covered during the course
Lectures, guided labs, homework assignments
For a detailed bibliograpy please refer to the course Aulaweb page (from https://corsi.unige.it/9269#chapter-5 open Manifesto degli Studi, look up Machine Learning and click it)
Office hours: All lecture days after class (approx. 20 min). Upon prior agreement, at any other time.
STEFANO ROVETTA (President)
FRANCESCO MASULLI (President Substitute)
All class schedules are posted on the EasyAcademy portal.
Written quiz, homework assignments
Evaluation of homeworks (50%)
Quiz grading (50%)
|10/01/2022||14:00||LA SPEZIA||Scritto + Orale|
|08/02/2022||14:00||LA SPEZIA||Scritto + Orale|
|17/06/2022||14:00||LA SPEZIA||Scritto + Orale|
|30/06/2022||14:00||LA SPEZIA||Scritto + Orale|
|06/09/2022||14:00||LA SPEZIA||Scritto + Orale|