Computational imaging (CI) integrates computer algorithms with imaging sensors to capture and process the physical world. It has a wide range of applications, ranging from cell phone cameras and AR/VR displays to autonomous driving, computed tomography (CT), magnetic resonance imaging (MRI), microscopy, and even black-hole imaging. In this course, we aim to present a unified formulation of different CI problems through the lens of inverse problems, introduce classic and modern algorithms for solving these inverse problems, and apply these algorithms to real-world imaging challenges. Topics include: a) the fundamentals of inverse problem and Bayesian inference, b) algorithms such as proximal gradient methods, alternating direction method of multipliers, total variation, deep neural networks, and diffusion models, and c) applications in image denoising, deblurring, super-resolution, CT, MRI, and optical microscopy. This course will introduce the basics of optimization and image processing as needed.