This course under the umbrella of "Topics in Science and Technology" aims to strengthen basic AI literacy: an understanding of how computers are used to collect data, process it, and make predictions or generate new data.

The course is held in six meetings, forming three topic groups:

1. Databases and Web Technology (15.10. + 22.10.)

By dissecting two clones of popular web platforms, we will see how intricately they are connected to databases, how the design of relational databases enforces a particular view of the world, and how exactly cookies can be (or could be) used to collect large amounts of data about users.

2. Classification and Neural Networks (19.11. + 26.11.)

Starting from a use case in one of the web platforms, we will reinvent two "classic" classification algorithms, and then move on to artificial neural networks. We will train a neural network for a custom task, and look behind the scenes to clarify there is absolutely no magic involved.

3. Algorithmic Bias and Generative AI (14.01. + 21.01.)

We will look at well-documented cases of algorithmic bias in real-world applications to understand what algorithmic bias is, how it occurs and why it can be difficult to avoid. With this in mind, we will take a critical look at large models trained to produce text or images, both to understand how they work and to uncover some of their shortcomings.

25% Active participation on at least five of six dates
75% Graded homework, handed out after each of the three topic groups, each due within 14 days, each worth 25%

For each date, please bring your laptops, if possible, to be able to participate in activities.

Students from other departments or universities will receive a place on the course subject to room capacities.