Machine Learning for Scientific Discovery
Event details
| Date | 25.11.2016 |
| Hour | 10:00 › 11:00 |
| Speaker |
Prof. Cheng Soon Ong Associate Professor, Australian National University |
| Location | |
| Category | Conferences - Seminars |
Advances in algorithms and computation have allowed researchers to analyse ever larger quantities of data. This has resulted in an increasingly data driven approach to scientific research for which machine learning turns out to be a popular paradigm. In recent years, we have developed new optimisation methods for solving problems such as ranking, learning from weak supervision and experimental design, with the aim of solving scientific questions in collaboration with experts in other fields. The long term research goal is to use active learning, bandits, and choice theory for designing experiments.
This high level talk is about a personal journey of discovery in the sciences through the lens of machine learning. After introducing a few basic machine learning ideas, I will highlight collaborations with experts in genomics, systems biology and astronomy that show how advances in data analysis have enabled scientific discovery. The focus will be on the practical challenges of interdisciplinary projects, and strategies for using machine learning in data driven science.
This high level talk is about a personal journey of discovery in the sciences through the lens of machine learning. After introducing a few basic machine learning ideas, I will highlight collaborations with experts in genomics, systems biology and astronomy that show how advances in data analysis have enabled scientific discovery. The focus will be on the practical challenges of interdisciplinary projects, and strategies for using machine learning in data driven science.
Practical information
- General public
- Free
Organizer
- Dr. Olivier Verscheure, Swiss Data Science Center, SDSC