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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
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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 2018/2019
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 055134
Course Title LEARNING SPARSE REPRESENTATIONS FOR IMAGE AND SIGNAL MODELING
Course Type Mono-Disciplinary Course
Credits (CFU / ECTS) 5.0
Semester Annual course
Course Description 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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manifesti v. 3.1.9 / 3.1.9
Area Servizi ICT
07/12/2019