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.
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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)
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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.
| Semester (Sem) | | 1 | First Semester | | 2 | Second Semester | | A | Annual course | Educational activities | | C | Similar 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
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Academic Year
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2015/2016
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School
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School of Industrial and Information Engineering
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Name
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(Master of Science degree)(ord. 270) - MI (481) Computer Science and Engineering
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Track
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T2A - COMPUTER SCIENCE AND ENGINEERING
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Programme Year
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1
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ID Code
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089234
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Course Title
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MODEL IDENTIFICATION AND DATA ANALYSIS
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Course Type
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Integrated Course
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Credits (CFU / ECTS)
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10.0
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Semester
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Second Semester
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Course Description
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In the models used in Engineering and Science, one often encounters uncertain elements. The aim of this course is to present the methods which can be used to estimate uncertain parameters and predict un-measurable signals from experimental data. Both batch and recursive methods are introduced. More precisely, we present very powerful identification techniques for the estimation of dynamical models in input-output description (Least Squares, Maximum Likelihood, etc), and for the estimation of the matrices of state space models via the Hankel matrix. Furthermore, we discuss the Kalman filter, a virtual sensor enabling the tracking of unmeasurable signal, which plays a major role in contemporary engineering. Real time identification techniques are also extremely useful to work out self-tuning predictors and governors, and the issue of adaptive systems is also touched upon. The application of the id methods to simple prediction and control problem is discussed with the contribution of scholars from industry and research centers.
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Scientific-Disciplinary Sector (SSD)
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Educational activities
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SSD Code
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SSD Description
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CFU
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C
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ING-INF/04
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SYSTEMS AND CONTROL ENGINEERING
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10.0
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Details 
Class schedule planning 