Content
Past few years have seen confluence of two related trends: 1/ A rapid adoption of Artificial Intelligence (AI) and Machine Learning (ML) in a wide range of real-world applications across a variety of domains, e.g., healthcare, engineering. 2/ Development of specialized tooling and design patterns for AI/ML workloads.
The goal of this course is to introduce the students to various AI/ML prediction paradigms, popular frameworks and design patterns. Specifically, we will build code bases involving (shallow) classification / regression models, CNNs and Transformers using frameworks like scikit-learn, PyTorch and Transformers. We will learn about using data loaders to manage large scale dataset and using GPUs to speed up deep learning workloads. We will also learn about best practices like testing and reproducibility.
Details
Lecture and Exercise:
- Thu, 10am-2pm
Lecturer:
- Bilal Zafar
- Nils Jansen
- Kursleiter/in: Miriam Ackermann
- Kursleiter/in: Nils Jansen
- Kursleiter/in: Elisabeth Kirsten
- Kursleiter/in: Marcel Neuhausen
- Kursleiter/in: Joshua Wendland
- Kursleiter/in: Muhammad Zafar