| The field of digital image analysis is growing rapidly in many application areas such as manufacturing, medicine, agriculture, transportation, and communication systems. This course aims to introduce the foundations of deep learning for digital image analysis from the fundamental theoretical concepts to applications. The class will provide students with an opportunity to explore the state-of-the-art deep learning models for object detection, image classification, and image segmentation with practical hands-on experience. The challenges of deep learning in the field of digital image analysis will be discussed in this class. The course will also explore some popular image datasets such as MNIST, CIFAR-10, and ImageNet, covering the image data preparation and labeling for deep learning. Evaluation metrics for deep learning models will be examined in detail. Students are free to use any programming language, however the programming examples in this course will be given in Matlab and Python. |