The following plan is preliminary and subject to change.

For the analytical parts of the lecture, we will mostly follow the textbook Advanced Engineering Mathematics by Erwin Kreyszig, 10th edition, John Wiley & Sons, 2011, and the chapter numbers in the table below refer to this textbook.

We have also used Morten Nome's excellent lecture notes from 2019 edition of TMA4215 and the material in Anne Kvœrnø's Jupyter Notebooks.

The numerical parts are based on our own notes; we will publish them during the course of the semester. To be able to be up and running with the code, refer to the guidelines present in the section Exercises

The fastest way to get started with Python and the Jupyter ecosystem is to use our Jupyterhub, which provides a cloud-based Jupyter environment which you can simply use in your browser. Just click on the link, enter your NTNU/Feide credantials and voilà, a Jupyter notebook application will show up in your browser tab.

If you prefer a local installtion, the easiest way to obtain installation of a full-fledged Python distribution which includes the most important scientific computing packages and a Jupyter environment, is to install the Anaconda Python Distribution. Detailed instructions for installing Anaconda on Windows, Linux and MacOS can be found in the Anaconda Documentation.

If you want to quickly brush up your Python, we recommend you to take a look at https://www.learnpython.org/, but if you are more interested into a deep-dive into Python, you might want to visit https://www.w3schools.com/python/default.asp. In particular, there are detailed tutorials on three most important Python modules we will use throughout the course:

• NumPy, a Python library/module used for working with arrays
• SciPy, a Python library/module for scientific computing
• Matplotlib, a Python library/module for visualization of scientific data.

Detailed documentation for the most fundamental Python libraries in scientific computations can be found at https://scipy.org/.