Demystifying CNNs: the Role of Domain Knowledge in Enhancing Interpretability and Efficiency [lecture Notes]

The development of Convolutional Neural Network (CNN) architectures has traditionally relied on ad hoc and brute force approaches, often with limited justification for design choices. This lack of theoretical grounding has been detrimental to their broader adoption in critical domains such as healthcare, finance, and energy systems. To this end, we revisit the operation of CNNs through the lens of matched filtering, a classical signal detection technique rooted in systems science. Such conceptual insight provides a first-principles framework for understanding CNN functionality, enabling the interpretability of the canonical convolution-activation-pooling pipeline, and facilitating their informed design. Moreover, this perspective supports domain-aware initialisation, suggesting ways to improve architectural efficiency and accelerate convergence. It is our hope that this Lecture Note will help establish a seamless bridge between well-established signal processing principles and the often opaque methodologies of deep learning, thereby demystifying CNNs for data analytics practitioners and for educational purposes.