270095 UE Machine learning for molecules and materials (2024W)

The aim of this seminar is the computer implementation and application of the ML algorithms discussed in the lecture. We will make use of the most common Python packages in the field.

This course provides a comprehensive introduction to machine learning, covering fundamental concepts and techniques used in the field. Students will explore supervised and unsupervised learning methods, including regression, classification, clustering, and dimensionality reduction. Key topics include overfitting, model evaluation, and the bias-variance tradeoff.

The course also delves into advanced methods such as neural networks and probabilistic approaches like Gaussian processes. Practical applications and hands-on exercises will enable students to implement and experiment with various machine learning algorithms using popular libraries. By the end of the course, students will be equipped with the knowledge and skills to tackle real-world machine learning problems and understand the principles behind the algorithms they use.

The participation in this course is only recommended in combination with 270087-1.