About this Lecture

Welcome to the 3rd edition of the Introduction to Scientific Machine Learning for Engineers in the winter semester 2023/2024! We are looking forward to a hopefully great semester, and to excite as many of you as possible for Scientific Machine Learning.

The course breaks down into an introduction to the topic, followed by 4 core content blocks which are interspersed with practice problems while being supported by JuPyter notebook-based tutorials for the practical application of the learned concepts.

Lecturers

• Nikolaus A. Adams

• Artur Toshev (artur.toshev@tum.de)

Questions should preferably be posted in the Moodle, or else be sent to Artur Toshev.

Outline

• Introduction

Lecture

• 1. Linear Models
• 2. Gaussian Mixture Models
• 3. Bayesian methods
• 4. Optimization
• 5. Tricks of Optimization
• 6. Support Vector Machines
• 7. Gaussian Processes
• 8. Gradients
• 9. Multilayer Perceptron
• 10. Convolutional Neural Networks
• 11. Recurrent Models
• 12. Encoder-Decoder Models

Exercise

• 1. Linear and Logistic Regression
• 2. Bayesian Linear Models
• 3. Optimization
• 4. Support Vector Machines
• 5. Gaussian Processes
• 6. Convolutional Neural Networks
• 7. Recurrent Neural Networks

Miscellaneous

• Admin
• Books
• References
• Preliminary Knowledge
• Software Infrastructure
• Practical Exam WS22/23
• Frequently Asked Questions

Contributors

Thanks to all contributors! Github names, if available.

Contributed content

1. Armin Illerhaus - Notebooks on Windows

Content fixes

1. Andreas Steger (AndSte01)

2. Muhammet Ali Güldali

Citation

Please cite this work as:

@article{paehler2023sciml,
  title={Introduction to Scientific Machine Learning for Engineers},
  author={Ludger Paehler and Artur P Toshev and Nikolaus A Adams},
  url={https://tumaer.github.io/SciML/about.html},
  year={2023},
}