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Semester (Sem)
1First Semester
2Second Semester
AAnnual course
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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 2016/2017
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 2

Course Details
ID Code 050694
Course Title LEARNING SPARSE REPRESENTATIONS FOR IMAGE AND SIGNAL MODELING
Course Type Mono-Disciplinary Course
Credits (CFU / ECTS) 5.0
Semester --
Course Description The goals 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. Students will have the opportunity to develop and understand the main algorithms for learning sparse models and computing sparse representations. Given the wide applicability of these methods, this course will be useful in their research. The goals of this course are: ● Present the most important aspects of the theory underlying sparse representations, and in particular the sparse‐coding and dictionary‐learning problems. ● Illustrate 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. ● Provide a broad overview of the applications involving sparse representations, in particular those concerning the imaging field. ● Provide students with a solid understanding of sparse representations by means of guided computer laboratory hours, where the presented algorithms will be implemented and tested in image denoising and inpainting applications. ● Provide an overview of sparsity as a general regularization prior and introduce recent developments such as convolutional sparsity.

Schedule, add and removeAlphabetical groupProfessorLanguageCourse details
From (included)To (excluded)
--AZZZZBoracchi Giacomo
manifesti v. 3.4.3 / 3.4.3
Area Servizi ICT
26/10/2020