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Machine learning meets statistics: Guiding medicine into the future

March 25 @ 9:00 am - March 26 @ 5:00 pm

SOLD OUT

SOLD OUT

Joint workshop, organised by The Alan Turing Institute and MRC Biostatistics Unit, University of Cambridge

Traditional statistical analysis and machine learning have many things in common but usually follow paths that are quite different. For prediction, traditional statistical analysis usually begins with a theory and a model and fits the parameters of the model to the data; machine learning follows a more pragmatic approach, allowing the data more freedom to prescribe the model.  Machine learning often leads to prognostic models that are more accurate but less interpretable.  For medical discovery, traditional statistical analysis usually begins by formulating a hypothesis and testing (the likelihood of) that hypothesis against the data; machine learning asks the data to formulate the (most likely) hypothesis. There are many synergies between these two disciplines and there is enormous potential in developing new fundamental theories and practical methods that transcend the boundaries of these disciplines leading to new and impactful methods to assist clinical practice and medical discovery. This workshop aims to foster such a dialogue and provide a forum for starting collaborations and for cross-fertilisation.

Topics: 

  • Learning and Inference from a Multitude of Data Sources
  • Dynamic Risk Prediction 
  • Early Disease Detection
  • Causal Inference and Individualised Treatment Effects
  • Rethinking Clinical Trials
  • Clinical Recommender Systems

Keynote speakers:

  • Oriol Vinyals (London DeepMind)
  • David Sontag (MIT Statistics and Data Science Center, USA)

Invited speakers:

  • David Alexander (University College London)
  • Jessica Barrett (University of Cambridge)
  • Franck Bretz (Novartis, Basel)
  • Sach Mukherjee (DZNE, Bonn)
  • Aditya Nori (Microsoft)
  • Blanca Rodriguez (University of Oxford)
  • Rajen Shah (University of Cambridge)
  • Sofia Villar (University of Cambridge)
  • Chris Williams (University of Edinburgh)
  • Chris Yau (University of Birmingham)
  • Serena Yeung (Harvard University)

Further information: https://www.turing.ac.uk/events/machine-learning-meets-statistics 

Details

Start:
March 25 @ 9:00 am
End:
March 26 @ 5:00 pm
Cost:
SOLD OUT
Event Category:
Website:
https://www.turing.ac.uk/events/machine-learning-meets-statistics

Venue

Clifford Allbutt Lecture Theatre
307 Hills Road
Cambridge, CB2 0XY United Kingdom
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