Deep Learning Applications for Computer Vision

In this course, you will be introduced to Computer Vision as a field of study and research. We will first examine several computer vision problems and proposed approaches to them from a classical computer vision perspective. We will then introduce Deep Learning methods and apply them to some of the same problems. We will analyze the results obtained and discuss the advantages and disadvantages of both types of methods. We will use tutorials so that you can practice some of the modern machine learning tools and software libraries. Examples of computer vision problems where deep learning can be applied include: image classification, image classification with localization, object detection, object segmentation, face recognition, activity or pose estimation. This course can be taken for academic credit toward CU Boulder’s MS in Data Science or MS in Computer Science programs, both offered on the Coursera platform. These fully accredited degrees offer focused courses, short 8-week sessions, and a fee-based tuition. Admission to the program is based on three prerequisite courses, not academic history. CU degrees on Coursera are ideal for recent graduates or working professionals.