Autumn School in Data Science: Machine learning applications for life sciences
Organizer: University of Cambridge
Host institution: University of Cambridge Bioinformatics Training
Start: Monday, 23 September 2019 @ 10:30
End: Thursday, 26 September 2019 @ 14:00
Venue: Craik-Marshall Building
Country: United Kingdom
Postcode: CB2 3AR
Scientific topic: Machine learning, BioinformaticsTarget audience:
- Students and researchers from life-sciences or biomedical backgrounds
- who have
- or will shortly have
- the need to apply the techniques presented during the course to biomedical data.
- The course is open to Graduate students
- Postdocs and Staff members from the University of Cambridge
- Institutions and other external Institutions or individuals
- <span style="color:#FF0000">Please note that all participants attending this course will be charged a registration fee. <span style="color:#0000FF"> Non-members of the University of Cambridge to pay £350. </span style> <span style="color:#0000FF">All Members of the University of Cambridge to pay £175. </span style> <span style="color:#FF0000">A booking will only be approved and confirmed once the fee has been paid in full.</span style>
THIS EVENT IS NOW FULLY BOOKED!
This Autumn School aims to familiarise biomedical students and researchers with principles of Data Science. Focusing on utilising machine learning algorithms to handle biomedical data, it will cover: effects of experimental design, data readiness, pipeline implementations, machine learning in Python, and related statistics, as well as Gaussian Process models.
Providing practical experience in the implementation of machine learning methods relevant to biomedical applications, including Gaussian processes, we will illustrate best practices that should be adopted in order to enable reproducibility in any data science application.
This event is sponsored by Cambridge Big Data.
The training room is located on the first floor and there is currently no wheelchair or level access available to this level.
- Workshops and courses