Courses Previous

Under the umbrella of “Advanced Topics in CS”, we bring this new course in which we cover diffusion based algorithms for data synthesis. This is a hot topic in machine learning and image processing, offering iterative methods that start form random noise and end with a high quality synthesis of visual (or other!) content. This course will be given in a seminar format, in which each participant studies a specific topic/paper and lectures about it to the class.

This is a graduate version of the course Numerical Algorithms (234125), covering the same material exactly, but ending with a large project in which a recent paper on the topics of this course is used for a final project. This project will require (i) reading the paper, implementing it, and extending its ideas; (ii) creation of slides to describe the paper’s content and results; (iii) lectruring on this paper to the course participants; and (iv) writing a final report to summarize this project.

This course focuses on sparse representations and their uses in signal and image processing and machine learning. 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), applications in image processing (denoising, inpainting, deblurring, compression), and connection to machine learning topics. The coursed has a unique format as it combines A MOOC (via EdX) and followup flipped-classrom meetings in class.

*** As oposed to previous years, this version of the course will not include a final project, but rather a final exam ***

A mandatory undergraduate course on numerical analysis, with emphasis on Numerical Linear Algebra. The course covers the following topics: LU and Cholesky factorization, 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.

A mandatory undergraduate course on numerical analysis, with emphasis on Numerical Linear Algebra. The course covers the following topics: LU and Cholesky factorizationד, 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.

A MOOC (via EdX) course on sparse representations and their uses in signal and image processing and machine learning. 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), applications in image processing (denoising, inpainting, deblurring, compression), and connection to machine learning topics.

A mandatory undergraduate course on numerical analysis, with emphasis on Numerical Linear Algebra. The course covers the following topics: LU and Cholesky factorizationד, 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.

*** This semester I will be recording the tutorial classes of this course.

A MOOC (via EdX) course on sparse representations and their uses in signal and image processing and machine learning. 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), applications in image processing (denoising, inpainting, deblurring, compression), and connection to machine learning topics.

A mandatory undergraduate course on numerical analysis, with emphasis on Numerical Linear Algebra. The course covers the following topics: LU and Cholesky factorizationד, 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.

*** This semester I will be teaching the tutorial classes in order to clean up and update their material, and prepare these for recording next semester.

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).

A mandatory undergraduate course on numerical analysis, with emphasis on Numerical Linear Algebra. The course covers the following topics: LU and Cholesky factorizationד, 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.

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).

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).

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).

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.

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).

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).

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).

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).

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.

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).

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

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.