This is an advanced undergraduate level course on the concepts, algorithms and system design in computer vision. The particular focus in this course is on the underlying computational/mathematical principles, and data-driven and neural networks (aka “deep learning”) approaches. The course introduces different computer vision tasks such as image/video classification, localization, detection, among others, and discusses different computational algorithms for these tasks, including recently proposed deep learning methods: convolutional neural networks (CNN), recurrent neural networks (RNN), long short-term memory (LSTM), Generative Adversarial Networks (GAN), etc. Students will learn to design, implement, train and debug their own systems and neural networks, and gain understanding of, and the skills to use, cutting-edge technologies in computer vision. A semester-long, 1-D design project requires students to design, implement, and train multi-million parameter neural networks to address real-world computer vision problems.
This course introduces the concept of artificial intelligence through machine learning and deep learning algorithms. In evidence‐based medicine, it is important to gather useful insights from both structured (e.g. data collected by machines) and unstructured data (e.g. notes from clinicians’ observations). The course highlights the use of AI for medical diagnostics, doctor consultation, personalized disease treatments, electronic health records, drug discovery and others. This poses both opportunities and challenges to gather buy‐in from various stakeholders, namely patients, doctors, researchers, and telehealth. In their various group projects, students will get to work on real‐life healthcare problems faced in hospitals and use AI to solve them.