Courses

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Teaching grades so far are found HERE (in Hebrew, where 1 stands for the lowest quality and 5 the highest, the left-most column is the overall grade).

236862Sparse and Redundant Representations and their Applications in Signal and Image Processing
Semester:
Winter 2018/2019
Description:

A MOOC (via EdX) course on sparse representations and their uses in signal and image processing. The course covers theoretical aspects of this field (e.g. uniqueness of sparse representation, pursuit performance), practical issues (e.g. dictionary learning, efficient numerical schemes for pursuit), and applications in image processing (denoising, inpainting, deblurring, compression).

234125Numerical Algorithms
Semester:
Spring 2018
Description:

A mandatory undergraduate course on numerical analysis, with emphasis on Numerical Linear Algebra. The course covers the following topics: LU and Cholesky factorization◊ď, Least-Squares, Gram-Schmidt algorithms and QR decomposition, eigenvalues and SVD, iterative methods for solving linear systems of equations, iterative methods for LS, iterative methods for finding eigenvalues and eigen-vectors, numerical errors and their effect, and an introduction to the discrete Fourier analysis via Circulant matrices.

236862Sparse and Redundant Representations and their Applications in Signal and Image Processing
Semester:
Winter 2017/8
Description:

A MOOC (via EdX) course on sparse representations and their uses in signal and image processing. The course covers theoretical aspects of this field (e.g. uniqueness of sparse representation, pursuit performance), practical issues (e.g. dictionary learning, efficient numerical schemes for pursuit), and applications in image processing (denoising, inpainting, deblurring, compression).

234125Numerical Algorithms
Semester:
Winter 2017/8
Description:

A mandatory undergraduate course on numerical analysis, with emphasis on Numerical Linear Algenra. The course covers the following topics: LU factorization, Least-Squares, QR decomposition, eigenvalues and SVD, iterative methods for solving linear systems of equations, iterative methods for LS, iterative methods for finding eigenvalues, iterative methods for solving general non-linear equations, numerical errors and their effect, and introduction to Fourier analysis. This course is a replacement for the Numerical ANalysis 1 coiurse (234107).

236862Sparse and Redundant Representations and their Applications in Signal and Image Processing
Semester:
Winter 2015/6
Description:

A graduate course on sparse representations and their uses in signal and image processing. The course covers theoretical aspects of this field (e.g. uniqueness of sparse representation, pursuit performance), practical issues (e.g. dictionary learning, efficient numerical schemes for pursuit), and applications in image processing (denoising, inpainting, deblurring, compression).

234107Numerical Analysis 1
Semester:
Winter 2014/5
Description:

A mandatory undergraduate course on numerical analysis. This semester the format of the course has changed — the second half of the course is given by me, and it is focused on Numerical Linear Algebra (NLA), covering topics such as LU factorization, Least-Squares, QR decomposition, eigenvalues and SVD, iterative methods for solving linear systems of equations, iterative methods for LS, iterative methods for finding eigenvalues, and possibly (if time permits), introduction to Fourier analysis.

236862Sparse and Redundant Representations and their Applications in Signal and Image Processing
Semester:
Winter 2014/5
Description:

A graduate course on sparse representations and their uses in signal and image processing. The course covers theoretical aspects of this field (e.g. uniqueness of sparse representation, pursuit performance), practical issues (e.g. dictionary learning, efficient numerical schemes for pursuit), and applications in image processing (denoising, inpainting, deblurring, compression).

236862Sparse and Redundant Representations and THeir Applications in Signal and Image Processing
Semester:
Winter 2013/4
Description:

A graduate course on sparse representations and their uses in signal and image processing. The course covers theoretical aspects of this field (e.g. uniqueness of sparse representation, pursuit performance), practical issues (e.g. dictionary learning, efficient numerical schemes for pursuit), and applications in image processing (denoising, inpainting, deblurring, compression).

236862Sparse and Redundant Representations and THeir Applications in Signal and Image Processing
Semester:
Winter 2012
Description:

A graduate course on sparse representations and their uses in signal and image processing. The course covers theoretical aspects of this field (e.g. uniqueness of sparse representation, pursuit performance), practical issues (e.g. dictionary learning, efficient numerical schemes for pursuit), and applications in image processing (denoising, inpainting, deblurring, compression).

236603Advanced Topics in Image Processing - Sparse Representations
Semester:
Winter 2011
Description:

A graduate course on sparse representations and their uses in signal and image processing. The course covers theoretical aspects of this field (e.g. uniqueness of sparse representation, pursuit performance), practical issues (e.g. dictionary learning, efficient numerical schemes for pursuit), and applications in image processing (denoising, inpainting, deblurring, compression).

236860Image Processing
Semester:
Spring 2010
Description:

An introductory course on image processing, covering the following topics: Mathematical signal processing in 2D, sampling and reconstruction, scalar/vector quantization and color representation, image restoration, transforms, image compression, image sequence processing, introduction to tomography, image pyramids, color theory.

236601Advanced Topics in Image Processing - Sparse Representations
Semester:
Winter 2010
Description:

A graduate course on sparse representations and their uses in signal and image processing. The course covers theoretical aspects of this field (e.g. uniqueness of sparse representation, pursuit performance), practical issues (e.g. dictionary learning, efficient numerical schemes for pursuit), and applications in image processing (denoising, inpainting, deblurring, compression).

236601Advanced Topics in image Processing - Sparse Representations
Semester:
Winter 2009
236860Image Processing
Semester:
Spring 2008
234114/7Introduction to Computer-Science
Semester:
Winter 2006/7/8
Description:

An introductory (first year) course to C programming, algorithms and their complexity.

236601Advanced Topics in Image Processing - Sparse Representations
Semester:
Spring 2006
0234299Mathematical Methods for Computer Applications
Semester:
Spring 2006
Description:

An undergraduate/graduate course on advanced mathematical tools, covering matrix factorizations (LU, LDV, Cholesky, QR, diagonalization, SVD), iterative methods for sets of equations, optimization, introduction to ODE’s and PDE’s.

236860Image Processing
Semester:
Winter 2005/6
236327Signal and Image Processing by Computer
Semester:
Spring 2005
234299Mathematical Methods for Computer Applications
Semester:
Spring 2005
236860Image Processing
Semester:
Winter 2004/5
236327Signal and Image Processing by Computer
Semester:
Spring 2003/4
236860Image Processing
Semester:
Winter 2003/4