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 | 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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2018/2019
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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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2
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ID Code
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055134
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Course Title
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LEARNING SPARSE REPRESENTATIONS FOR IMAGE AND SIGNAL MODELING
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Course Type
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Mono-Disciplinary Course
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Credits (CFU / ECTS)
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5.0
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Semester
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Annual course
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Course Description
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The main goal of this course is to provide the student with an understanding of the most important aspects of the theory underlying sparse representation and, more in general, of sparsity as a form of regularization in learning problems. Students will have the opportunity to develop and understand the main algorithms for 1) learning sparse models, 2) computing sparse representations, and 3) solve optimization problems involving sparsity as a regularization prior. These methods have wide applicability in computer science, and these will be a useful background for their research.
In particular, this course aims at:
● Presenting the most important aspects of the theory underlying sparse representations, and in particular the sparse-coding and dictionary-learning problems.
● Illustrating the main algorithms for sparse coding and dictionary learning, with a particular emphasis on solutions of convex optimization problems that are widely encountered in engineering.
● Providing an overview of sparsity as a general regularization prior in many inverse problems and the connection with LASSO in linear regression.
● Providing a broad overview of the applications involving sparse representations, with particular emphasis in image denoising (unsupervised task) and image classification (supervised task).
● Providing a solid understanding of sparse representation algorithms by means of guided computer laboratory hours, where students will implement and test these algorithms.
● Introduce extended models such as double sparsity and convolutional sparsity.
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| Schedule, add and remove | Alphabetical group | Professor | Language | Course details |
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| From (included) | To (excluded) |
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| -- | -- | -- | Docente non definito | | -- |
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Details 
Class schedule planning