Linear Algebra Lecture
Many CVML scientists, engineers and enthusiasts do not have solid mathematical background, as it is so easy to jump into almost any CVML domain using available libraries and frameworks. This is very much true in Deep Learning and leads to a cacophony of inaccurate statements and a polyphony of ill-defined terms and concept. Therefore, a rigorous mathematical background is a must for anybody working in this area. Luckily, most ECE/CS curricula provide such foundations.

This lecture overviews Linear Algebra that has many applications in Machine Learning, Computer Vision and Scientific Computing. It covers the following topics in detail: Vectors, matrices, System of linear equations, Eigenanalysis, Singular value Decomposition, Other matrix decompositions, Tensors Fundamentals, Tensor decompositions, BLAS.