EW-SEGS-D-KU-GGKB, WS 25/26 Grundlagen der Gestaltung und Kunstbetrachtung / dienstags

This lecture series provides a comprehensive introduction to machine learning techniques and their application to challenges in the natural sciences. Starting with foundational concepts like classification and regression, the course prog
resses through key models including tree-based methods, logistic regression, artificial neural networks, convolutional neural networks (CNNs), and transformers. Students will gain hands-on experience implementing these methods using PyTorch and scikit-learn, culminating in a series of capstone projects designed to apply learned techniques to real-world scientific problems. Topics also include uncertainty quantification (UQ), explainable AI, and an exploration of cutting-edge generative models like variational autoencoders, diffusion models, and large language models (LLMs). This series aims to equip learners with the tools and knowledge to leverage the power of machine learning in their respective fields.

Planned Topics:

• Classification with Decision Trees and logistic regression
• Classification metrics and cross validation
• Classification: confusion matrix, ROC, (bias due to training set selection?)
• from classification to regression: MLE, least squares method, linear models
• regression: ANNs and SGD
• regression with pytorch
• activations functions and classification with pytorch
• convolution and CNNs
• ResNets and UNets
• Transposed Convolutions, Auto-Encoders and Variational Auto-Encoders
• Variational Auto-Encoders and Normalizing Flows
• Diffusion and FlowMatching
• Uncertainty Quantification
• Methods of Explainable ML
• ML with Text, Attention and Transformers
• Large Language Models (LLMs) and chatGPT

What do you gain from this lecture series:

• hands-on experience on training small and larger ML models
• theoretical overview and some deep dives on how learning in ML models work
• the possibility to perform your master exam in this subject