Apart from providing students with a deeper understanding of the theoretical knowledge of computer vision, machine learning and artificial intelligence - depth learning, and analyzing how the principles of depth learning combine with the development of artificial neural networks (ANNs) and computer vision, the functional aspects of this course will be explained by teacher’s more than 20 years of experience in industrial cooperation (face detection, recognition and expression analysis, intelligent video surveillance as a service, automated optical inspection (AOI), intelligent manufacturing, visual-guided robot arm control, and automatic guided vehicle (AGV)). The course will teach the basic computer vision principles and techniques for the relationship between 2D images and 3D objects with augmented reality (AR), including the calibration method of the camera and the reconstruction principle of 3D objects. Then I will teach the basic but practical techniques of computer vision, including the design of real-time detection, tracking and recognition systems. We will bring machine learning connections to the field of deep learning. Based on deep learning technology, I will teach you how to develop better real-time detection, tracking and recognition techniques to solve practical problems in the fields of industry, clinical imaging and AI sports. This course aims to develop students' ability to design and integrate skills in computer vision, machine learning and depth learning. Through practical practice, students' ability to think, program design and solve existing problems can be nurtured. Students' ability and spirit of team work can be nurtured through group work. They can also apply the theoretical foundation to industry, clinical imaging and precision sports analytics.