Inverse problems arise every time we want to infer information on quantities from their in- direct measurements. This is typical of many applications in different areas, like medical imaging, geophysics, engineering, remote sensing and environmental sciences, life sciences and many more. Usually, inverse problems are ill-posed: the solution can not exist, or can not be unique, or does not depend with continuity from the data. For these reasons, the treatment of ill-posed inverse problems requires sophisticated techniques. Our aim is to introduce some tools for the solution of ill-posed inverse problems. Both classical regularization methods and statistical methods are proposed. The last three talks will be dedicated to the study of three possible applications.