The goal of Artificial Intelligence (AI) is to enable machines to perform tasks that would typically require human intelligence, such as perception, reasoning, and learning. A recent example for the application of AI is given by image generators, such as stable diffusion, and text generators, such as ChatGPT. As a study topic, AI is highly interdisciplinary, drawing on programming, mathematics, and engineering.
In particular, mathematical skills are required to develop and understand the algorithms and models that underpin AI systems. For example, optimization techniques are used to find the optimal models, such as the one minimizing the prediction error. Statistics is necessary for analyzing and interpreting data, and for developing models that maintain desirable statistical properties. Linear algebra is used to manipulate and transform data, for example by the sequential application of linear functions with nonlinear activation in neural networks.
In addition to these core mathematical skills, AI researchers and practitioners need to have a strong foundation in programming, data structures, and algorithms. Finally, AI also involves ethical and social considerations, as intelligent machines have the potential to impact society in significant ways. As such, AI researchers and practitioners need to be aware of the ethical implications of their work and strive to develop AI systems that are fair, transparent, and accountable.
From September to November, we will try to have a very fast learning curve. After an introductory lecture into the topic of proposed projects, you are asked to get up to speed with the material that you require for your specific project. Maybe you can put hands on first experiences with your chosen topic, by running toy examples and making smaller experiments.
In the second year, we will assume that you have gained the necessary optimization and programming skills, and that you have a sufficient theoretical basis to delve deep into the topics that interest you. In your team, you can develop professional skills in the scope of management and leadership by helping the first-year students to achieve their goals. Concerning your topic, you can set up bigger experiments and draw the necessary conclusions from them.
AI is a broad multidisciplinary field. During this two-year track will be impossible to cover all its aspects, but you shall be able to “learn how to learn” yourself about any domain and to understand better what type of career you would like to follow after your bachelor studies (e.g. engineering, management , research, academia). Everyone can find her/his place in this honors track. You need just to be very self-motivated, to have a strong wish to succeed, to like self-study and team work at the same time, and to enjoy answering to questions like “How?” and “Why?”