logo-polimi
Loading...
Degree programme
Programme Structure
Show/Search Programme
Course Details
Save Document
Degree Programme
Read Degree Programme
Quantitative data
Faculty
Infrastructures
International context
Customized Schedule
Your customized time schedule has been disabled
Enable
Search
Search a Lecturer
Search a Course
Search a Course (system prior D.M. n. 509)
Search Lessons taught in English

Glossary
Semester (Sem)
1First Semester
2Second Semester
AAnnual course
Educational activities
CSimilar or integrative activities
Language
Course completely offered in italian
Course completely offered in english
--Not available
Innovative teaching
The credits shown next to this symbol indicate the part of the course CFUs provided with Innovative teaching.
These CFUs include:
  • Subject taught jointly with companies or organizations
  • Blended Learning & Flipped Classroom
  • Massive Open Online Courses (MOOC)
  • Soft Skills
Course Details
Context
Academic Year 2018/2019
School School of Industrial and Information Engineering
Name (Master of Science degree)(ord. 270) - MI (471) Biomedical Engineering
Track BCI - Ingegneria clinica - Clinical engineering
Programme Year 1

Course Details
ID Code 053458
Course Title MACHINE LEARNING
Course Type Mono-Disciplinary Course
Credits (CFU / ECTS) 5.0
Semester First Semester
Course Description The goal of the course is to provide the background for advanced modelling and data analysis, together with Kalman Filter techniques for parameters and virtual sensors estimation. The course is also intended to illustrate data mining concepts and methods, and to provide an introduction to optimization theory. The course has both a theoretical and a practical flavour, and is focused on the following topics: Stationary stochastic processes generated as output of dynamic systems. ARMA and ARMAX models. Prediction. Non-parametric models based on the spectral characteristics of a process. Estimation methods based on minimum prediction error. Model complexity analysis and parameters identification. Virtual sensors: Kalman Filter; Extended Kalman Filter for gray-box parameters identification. Data mining process. Exploratory data analysis, data preparation and feature selection techniques. Classification methods. Clustering. Association rules. Introduction to linear optimization.
Scientific-Disciplinary Sector (SSD)
Educational activities SSD Code SSD Description CFU
C
INF/01
INFORMATICS
5.0

Schedule, add and removeAlphabetical groupLecturer(s)LanguageTeaching Assignment Details
From (included)To (excluded)
---AZZZZGaratti Simone---
Vercellis Carlo---
manifesti v. 3.7.7 / 3.7.7
Area Servizi ICT
11/02/2025