DESCRIPTION
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 CVML Web Module focuses on CVML Mathematical Foundations.
Mathematical Analysis for single variable and multivariate functions is the indispensable basis for all CVML domains, as, e.g., images are 2D signals (functions). Linear Algebra provides the basic mathematical tools not only for Computer Vision but also for Signals and Systems and Machine/Deep Learning. Probability Theory is very important for Signal/Image Processing and Machine Learning, since all data can be described by probabilistic forms for Data Analytics. Geometry plays an important role not only in Computer Vision, where we do 3D world modeling, but also in creating many useful data/signal representations for Data Analytics. Set theory is very useful, as data (e.g., images) can be considered as sets and training data sets are the basis for Deep Learning.
LECTURE LIST
- Geometry
- Mathematical Analysis
- Linear Algebra
- Probability Theory
- Set Theory
MATERIAL
https://aiia.csd.auth.gr/cvml-mathematical-foundations-material/