Prof. Dr. Christoph Lippert

Teaching Activities

Repository: GitHub/HealthML

Deep Learning Lecture

This course is designed to give students an in-depth introduction to deep learning and review the state of the art in important applications, such as computer vision and language modeling.

The lectures and exercises are designed around fundamentals and use cases and will use real-world data to gain practical experience with machine learning models and algorithms.
The course introduces basic concepts of machine learning and empirical data analysis, such as model fitting, selection and validation. During the course, students discuss supervised machine learning, starting with linear models, to non-linear models, including deep neural networks, convolutional neural networks and sequence models.  Additionally the course discusses unsupervised learning, generative models and self-supervision.

For more resources see: here

Math for Machine Learning (M4ML)

Machine learning uses tools from a variety of mathematical fields.

During this applied mathematics course, we cover a summary of the mathematical tools from linear algebra, calculus, optimization and probability theory that are commonly used in the context of machine learning. Beyond providing the solid mathematical foundation that is required for machine learning, students learn to derive and discuss important machine learning concepts and algorithms.

For more resources see: here