Modern Deep Learning Learning for Physicists
NAT3008 is a semester module in language at which is offered irregularly.
This Module is included in the following catalogues within the study programs in physics.
- Specific catalogue of special courses for condensed matter physics
- Specific catalogue of special courses for nuclear, particle, and astrophysics
- Specific catalogue of special courses for Biophysics
- Specific catalogue of special courses for Applied and Engineering Physics
If not stated otherwise for export to a non-physics program the student workload is given in the following table.
Responsible coordinator of the module NAT3008 is Lukas Heinrich.
Content, Learning Outcome and Preconditions
After successful completion of the module the students are able to:
- Recall necessary Statistics, Learning Theory, and Array Programming
- Understand the Benefits of Deep Learning and Overparametrization with respect to other approaches to ML and AI.
- Have a Broad Overview over deep neural networks architectures (Recurrent Networks, Convolutional Networks, Attention, Transformers, Graph Neural Networks)
- Understand Generative Models (GANs, VAEs, Normalizing Flows, Diffusion Models, …)
- Understand Deep Learning Inference as Amortized Bayesian Analysis
- Use Differentiable Programming and Gradient-based Optimization
- Have an Overview over Machine Learning Frameworks
- Have an Overview over Applications in fundamental physics Research
- How to train deep neural networks for a variety of data modalities (image, sequences, structured data)
No preconditions in addition to the requirements for the Master’s program in Physics.
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
|Modern Deep Learning Learning for Physicists
Fri, 13:00–15:00, ZEI 0001
|Exercise to Modern Deep Learning Learning for Physicists
Responsible/Coordination: Heinrich, L.
|dates in groups
Learning and Teaching Methods
Description of exams and course work
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.
The exam may be repeated at the end of the semester.