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Courses

Courses

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).

Sparse and Redundant Representations and their Applications in Signal and Image Processing (236862, Winter 2017/8)

MOOC MOOC MOOC MOOC MOOC MOOC MOOC MOOC MOOC MOOC MOOC MOOC MOOC MOOC MOOC MOOC MOOC MOOC MOOC MOOC MOOC MOOC MOOC MOOC

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).

MOOC MOOC MOOC MOOC MOOC MOOC MOOC MOOC MOOC MOOC MOOC MOOC MOOC MOOC MOOC MOOC MOOC MOOC MOOC MOOC MOOC MOOC MOOC MOOC

Sparse and Redundant Representations and their Applications in Signal and Image Processing (236862, Winter 2015/6)

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).

Numerical Analysis 1 (234107, Winter 2014/5)

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.

Sparse and Redundant Representations and their Applications in Signal and Image Processing (236862, Winter 2014/5)

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).

Sparse and Redundant Representations and THeir Applications in Signal and Image Processing (236862, Winter 2013/4)

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).

Sparse and Redundant Representations and THeir Applications in Signal and Image Processing (236862, Winter 2012)

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).

Advanced Topics in Image Processing - Sparse Representations (236603, Winter 2011)

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).

Image Processing (236860, Spring 2010)

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.

Advanced Topics in Image Processing - Sparse Representations (236601, Winter 2010)

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).

Advanced Topics in image Processing - Sparse Representations (236601, Winter 2009)

Image Processing (236860, Spring 2008)

Introduction to Computer-Science (234114/7, Winter 2006/7/8)

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

Advanced Topics in Image Processing - Sparse Representations (236601, Spring 2006)

Mathematical Methods for Computer Applications (234299, Spring 2006)

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.

Image Processing (236860, Winter 2005/6)

Signal and Image Processing by Computer (236327, Spring 2005)

Mathematical Methods for Computer Applications (234299, Spring 2005)

Image Processing (236860, Winter 2004/5)

Signal and Image Processing by Computer (236327, Spring 2003/4)

Image Processing (236860, Winter 2003/4)