Modernes Deep Learning in der Physik
Modern Deep Learning Learning for Physicists

After successful completion of the module the students are able to:

1. Recall necessary Statistics, Learning Theory, and Array Programming
2. Understand the Benefits of Deep Learning and Overparametrization with respect to other approaches to ML and AI.
3. Have a Broad Overview over deep neural networks architectures (Recurrent Networks, Convolutional Networks, Attention, Transformers, Graph Neural Networks)
4. Understand Generative Models (GANs, VAEs, Normalizing Flows, Diffusion Models, …)
5. Understand Deep Learning Inference as Amortized Bayesian Analysis
6. Use Differentiable Programming and Gradient-based Optimization
7. Have an Overview over Machine Learning Frameworks
8. Have an Overview over Applications in fundamental physics Research
9. How to train deep neural networks for a variety of data modalities (image, sequences, structured data)

Keine Vorkenntnisse nötig, die über die Zulassungsvoraussetzungen zum Masterstudium hinausgehen.

There will be a written exam of 90 minutes duration. Therein the achievement of the competencies given in section learning outcome is tested exemplarily at least to the given cognition level using comprehension questions and sample calculations.

For example an assignment in the exam might be:

• Discuss how Deep Learning differentiates itself from other Types of Approaches to AI
• What's the Bias-Variance Tradeoff?

Participation in the exercise classes is strongly recommended since the exercises prepare for the problems of the exam and rehearse the specific competencies.