Mathematical Analysis 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 Mathematical Analysis that has many applications in Computer Vision, Machine Learning and Autonomous Systems. It covers the following topics in detail: 1D/2D/3D functions with applications in signal, image and video processing. Analytical and numerical differentiation of 1D functions. Analytical and numerical integration of 1D functions. Analytical and numerical partial differentiation of 2D/3D/spatiotemporal functions. Laplacian operator. Applications in image edge detection, partial differential equations and anisotropic diffusion.