Short Courses

During the workshop, the following 2 short courses are offered to the participants. They are an opportunity to introduce students or possibly interested researchers to inverse problems and to machine learning applied to some topics related to the workshop.

Course 1 Title: Machine Learning

Date: Wednesday 12th,Thursday 13th, Friday 14th, 15:00-15:45 (3 modules)

 Instructor: Francisco Sahli (PUC,Chile)


In this course we will cover the basics of machine learning and deep learning. In particular, we will learn about the busy terminology in these areas and its connections with statistical concepts, in addition to learning what neural networks are and how they are trained. The course will culminate with practical applications of these tools in a workshop.

Course 2 Title: Inverse Problems

Date: Wednesday 12th, 16:00-16:45, 17:00-17:45 , Friday 14th 16:00-16:45 (3 modules)

Instructor: Axel Osses (U. de Chile)

  • Wednesday 12th, 16:00-16:45 – theory (via Zoom)
  • Download the pdf presentation here

Assistant: Carlos Castillo (PUC)

  • Wednesday 12th, 17:00-17:45 – practical session 1 (via Discord)
  • Friday 14th 16:00-16:45 – practical session 2  (via Discord)


In this course some basic concepts of inverse problems will be reviewed from the point of view of parameter identification, data assimilation and regularization. The theoretical classes will be accompanied by practical laboratories with applications related to the management of images through jupyter notebooks.

For the practical sessions you will need:

  • a Google account for using Google colab. You do not need to install python or jupyter in your computer;
  • please install Discord in your computer in order to supervise your session, if you are a mac user please allow to record screen to Discord app in security preferences;
  • try with this Jupyter notebook (click and press in “Open with Google Colab”): here
  • read and explore this notebook a brief introduction to python and numpy: here


  • Steven L. Brunton, J. Nathan Kutz. Data Driven Science & Engineering. Machine Learning, Dynamical Systems and Control, 2017. Available online here
  • Per Christian Hansen, Discrete Inverse Problems: Insights and Algorithms, SIAM, Philadelphia, 2010
  • Mario Bertero, Patrizia Boccaci, Christine De Mol, Introduction to Inverse Problems in Imaging, Second Ed., CRC Press, Boca Raton, London, New York, 2021
  • Andreas Kirsch, An Introduction to the Mathematical Theory of Inverse Problems, Third Ed., Applied Mathematical Sciences Vol 120, Springer Nature Switzerland, 2021

Short Course Inverse Problems

Free Registration: