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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 2014/2015
School School of Industrial and Information Engineering
Name (Master of Science degree)(ord. 270) - MI (481) Computer Science and Engineering
Track T2A - COMPUTER SCIENCE AND ENGINEERING
Programme Year 1

Course Details
ID Code 088767
Course Title MODEL IDENTIFICATION AND DATA ANALYSIS 1
Course Type Mono-Disciplinary Course
Credits (CFU / ECTS) 5.0
Semester Second Semester
Course Description From Data to Model: Laws and models in engineering and science. Problems of prediction, time series analysis, clustering, control. Model accuracy versus complexity. Data treatment. Dynamical models for stationary processes, spectral analysis and prediction: Models for time series analysis and cause-effect systems (AR, MA, ARMA, ARX, ARMAX, BOX & JENKINS models). Correlation and spectral analysis. Kolmogorov-Wiener prediction. Simple non-linear models. Identification: Batch and recursive methods. Complexity selection. Yule-Walker equations and Durbin-Levinson algorithm. Spectral estimation from data. Use of models for minimum variance control. Applications: `Data mining` of WEBLOG files. Pattern recognition in bio-informatics. Data analysis for the best production of silicon wafers. Estimation of models for financial engineering. Identification and adaptive control of plants. Stochastic simulation. Lab activity: Data analysis and model identification are the subjects of many software tools available on the market and are extensively used in the work environment. The purpose of the lab activity is to expose the student to the main tools of this type. Thus any student will be presented some snapshots drawn from experimental data; from them the student`s task will be to estimate the parameters of a sensible model suitable in describing the underlying phenomenon or the systems, and then tackle problems of prediction, classification, control, etc. web site: www.elet.polimi.it\corsi\IMAD
Scientific-Disciplinary Sector (SSD)
Educational activities SSD Code SSD Description CFU
C
ING-INF/04
SYSTEMS AND CONTROL ENGINEERING
5.0

Schedule, add and removeAlphabetical groupProfessorLanguageCourse details
From (included)To (excluded)
--AZZZZBittanti Sergio
manifesti v. 3.1.9 / 3.1.9
Area Servizi ICT
16/12/2019