Lecture 01       Describing Inverse Problems
Lecture 02       Probability and Measurement Error, Part 1
Lecture 03       Probability and Measurement Error, Part 2
Lecture 04       The L2 Norm and Simple Least Squares
Lecture 05       A Priori Information and Weighted Least Squared
Lecture 06       Resolution and Generalized Inverses

Lecture 07       Backus-Gilbert Inverse and the Trade Off of Resolution and Variance
Lecture 08       The Principle of Maximum Likelihood
Lecture 09       Inexact Theories

Lecture 10       Prior Covariance and Gaussian Process Regression
Lecture 11       Non-uniqueness and Localized Averages
Lecture 12       Vector Spaces and Singular Value Decomposition

Lecture 13       Equality and Inequality Constraints
Lecture 14       L1 , L Norm Problems and Linear Programming
Lecture 15       Nonlinear Problems: Grid and Monte Carlo Searches
Lecture 16       Nonlinear Problems: Newton’s Method
Lecture 17       Nonlinear Problems:  MCMC and Bootstrap Confidence Intervals
Lecture 18       Factor Analysis
Lecture 19       Varimax Factors, Empirical Orthogonal Functions
Lecture 20       Backus-Gilbert Theory for Continuous Problems; Radon’s Problem
Lecture 21       Linear Operators and Their Adjoints
Lecture 22       Fréchet Derivatives

Lecture 23       Estimating a Parameter in a Differential Equation
Lecture 24       Exemplary Inverse Problems, incl. Filter Design
Lecture 25       Exemplary Inverse Problems, incl. Earthquake Location
Lecture 26       Exemplary Inverse Problems, incl. Vibrational Problems