With this function you can construct your weekly calendar of lessons, which is customized on the basis of the courses that you intend to follow. Warning: the personal schedule does not replace the presentation of the study plan! It's an informal tool that can help you better manage the organization of class attendance before the study plan presentation. After the study plan presentation we recommend you to use the Lecture timetable service in your Online Services.
To create your customized schedule follow these instructions:
Click on the "Enable" link to proceed. You will be asked your surname and first name in order to determine your alphabetic grouping.
To add or remove courses from your personal schedule, use the small icons which are found next to the courses:
addition of the course
removal of the course
selection of the section of the Laboratory of Architecture (Note: the effective area in which the teaching will be carried out will be determined after the presentation of the Study Plans)
The sidebar on the left displays the number of lessons included in schedule. There are also these commands:
View the schedule: allows the viewing of the weekly synoptic schedule
Delete the schedule: cancels the selections made
When you have finished the entry, you can print the calendar you have made.
Similar or integrative activities
Course completely offered in italian
Course completely offered in english
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)
School of Industrial and Information Engineering
(Master of Science degree)(ord. 270) - MI (481) Computer Science and Engineering
T2A - COMPUTER SCIENCE AND ENGINEERING
MODEL IDENTIFICATION AND DATA ANALYSIS 1
Credits (CFU / ECTS)
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