1. Introduction and Motivation
-What are Inverse problems?
-Examples: detection of contaminant sources, image and voice recognition, medical imaging, subsurface imaging, materials identification
2. Theoretical aspects of (discrete) inverse problems
-Why are inverse problems (oftentimes) difficult to solve?
-Well-posed and ill-posed problems: existence, uniqueness, and stability of solutions
-Linear vs nonlinear inverse problems
-Singular Value Decomposition: a path to understanding inverse problems