Prerequisites

Programming Preparation

Since the tutorials will be in Python, we recommend participants to get acquainted with Python syntax, so that they are able to follow the content. If Python is not your primary programming language we expect you to review this introductory Python programming lesson before the workshop. Typing out the commands in a Jupyter notebook is the best way to learn. The lesson provides instructions how to install Python on your own machine, or you can use a free computing environment such as Colab. You can also consider any other online resources for getting familiar with Python. You are welcome to use any programming language for project work but that would require finding suitable ML libraries and following their documentation. If you are planning to work with some specific data, ensure that you can read the data and make some plot with the language of your choice.

Machine Learning Preparation

We expect participants to have experience with fitting models to data, classic unsupervised/supervised techniques (e.g. PCA, logistic regression/random forests), evaluating performance. To refresh yourself on these topics you can review this introductory ML lesson (1-6). Although some problems are more suitable to be addressed directly with AI tools, understanding the fundamental concepts of machine learning is critical regardless of the method you use, and in fact we encourage you to always compare to simpler methods, which in practice can be sufficient.

Concerned About Your Technical Preparation?

We are not going to grade you! The goal is to learn! Reviewing the above lessons will help you be more productive during the workshop.

Package Management

Since the AI field changes so fast, packages change fast, and their dependencies change fast. As you are experimenting with new (or old) tools, sooner or later, whether during the workshop or afterwards in your research, you will encounter package compatibility issues. Those can be addressed by using virtual environments. You can visit this documentation to learn how to make virtual environments with mamba.