This course introduces the fundamentals of the Wavelet Transform as a powerful tool for time-frequency signal analysis. It covers both the Continuous Wavelet Transform (CWT) and the Discrete Wavelet Transform (DWT), highlighting their theoretical foundations, practical differences, and typical application scenarios. Particular attention is devoted to the interpretation of scalograms, their visual characteristics, and their conversion into image representations. The course concludes by exploring how wavelet-based images can be leveraged as input for modern deep learning models, including Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), enabling effective signal classification and pattern recognition across a wide range of applications.