
HGS MathComp - Where Methods Meet Applications
The Heidelberg Graduate School of Mathematical and Computational Methods for the Sciences (HGS MathComp) at Heidelberg University is one of the leading graduate schools in Germany focusing on the complex topic of Scientific Computing. Located in a vibrant research environment, the school offers a structured interdisciplinary education for PhD students. The program supports students in pursuing innovative PhD projects with a strong application-oriented focus, ranging from mathematics, computer science, bio/life-sciences, physics, and chemical engineering sciences to cultural heritage. A strong focus is put on the mathematical and computational foundations: the theoretical underpinnings and computational abstraction and conception.
HGS MathComp Principal Investigators are leading experts in their fields, working on projects that combine mathematical and computational methodology with topical research issues. Individual mentoring for PhD candidates and career development programs ensure that graduates are fully equipped to take up top positions in industry and academia.
Upcoming Events [see all...]
09:30 - 13:00
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Location: Mathematikon • Conference Room 5/104, 5th Floor • Im Neuenheimer Feld 205, 69120 Heidelberg
Registration: Please register via this form
Organizer: Scientific Software Center (SSC)
The latest information and a registration link are available on the course website.
This compact course is part of the course program of the Scientific Software Center (SSC) at Heidelberg University.
No prior knowledge by the participants is necessary to participate in this course and it is intended for all scientific audiences. Participants are required to bring their own laptops to work on during the course. Network access (e.g. through Eduroam) is recommended.
Summary
Jupyter notebooks are a great tool for exploring and interacting with data using the Python programming language and its rich ecosystem of libraries. In this course we will cover basic usage of the Pandas library to download a dataset, explore its contents, clean up missing or invalid data, filter the data according to different criteria, and plot visualizations of the data.
Learning Objectives
After the course participants will be able to use a Jupyter notebook to
- Explore a dataset
- Filter the dataset
- Clean up missing / invalid entries
- Generate plots and visualizations of the data
09:00 - 13:00
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Location: Online
Registration: Please register on the event website
Organizer: Graduate Academy
The latest information and a registration link are available on the course website (log in with Uni-ID).
HGS MathComp fellows can get a reimbursement of the course fees. Please submit your proof of payment and certificate of participation to hgs@iwr.uni-heidelberg.de.
All participants need a microphone and ideally a webcam.
Hardware integrated into the laptop is sufficient.
Topics (selection)
- Formats (blogs, news, opinion, etc.)
- Writing styles
- Elements of a good story
- Story structure
Common mistakes (passive voice, jargon, etc.)
09:00 - 13:00
[ more ...]
Location: Mathematikon • Conference Room 5/104, 5th Floor • Im Neuenheimer Feld 205, 69120 Heidelberg
Registration: Please register via this form
Organizer: Scientific Software Center (SSC)
The latest information and a registration link are available on the course website.
This compact course is part of the course program of the Scientific Software Center (SSC) at Heidelberg University.
Basic Python knowledge and knowledge about data processing, ML models and training of models is required.
Summary:
The AI revolution is moving even more rapidly than the digital revolution and leads to the emergence of completely new tools and technologies that affect the scientific process. In this course, we will learn about data-based research software, tools and communities that are relevant in creating and sharing such software, and about best practices in training machine-learning models. Research software that is based on ML models requires an additional layer of best practices in the implementation, including testing of non-deterministic processes. Security aspects as well as bad examples are discussed to highlight the importance of adhering to a best practices code of conduct.
Learning Objectives:
After the course participants will
- Understand and follow best practices in the underlying dataset
- Understand and follow best practices in training ML models
- Write better data-based research software, including appropriate tests
- Avoid negative impact from legal and security issues