This course provides an introduction to computer vision and deep learning, focusing on their applications in image processing and analysis. Students will explore key topics, including image preprocessing, dataset preparation, and foundational concepts in deep learning. The curriculum delves into advanced topics such as image classification, transfer learning, and deep learning model architectures. A special emphasis is placed on autoencoders, including their variants like variational (VAE) and convolutional (CAE) autoencoders, with practical applications in image denoising and anomaly detection. Critical discussions on bias in computer vision tasks and the ethical implications of Al systems are integrated into the learning experience. The course culminates in term projects, where students present innovative applications of deep learning in real-world scenarios.