Presentations CWI Lectures on Machine Learning 2017

Please find below more information about the presentations which will be held on 23 November:

 

Neil Lawrence
Personalized Health: Challenges in Data Science
The promise of personalized health is driven by the wide availability of data, but we don’t need to talk so much about where we want to be, rather
how we should get there. What are the challenges that need to be bridged technologically to unlock the potential in the much greater availability of
data we now have? In this talk we’ll consider three challenges of data science in the context of personalized health, the three challenges each
need to be bridged to bring the era of true precision, or personalized, medicine within the reach of an affordable health care service.





Suchi Saria

Machine learning and counterfactual reasoning for personalized clinical decision making
will follow asap

 


Max Welling
Data Efficient Deep Learning for medical Image Processing
Deep learning has been remarkable effective in the large data domain, but it's much more challenging to fit models reliably when there is few or
weakly labeled examples available. This is unfortunately the scenario we are facing when dealing with medical images for which detailed labeling is very expensive. In this talk I will provide some insights into how this can be improved, through e.g. semi-supervised learning, equivariant
convolutions or data augmentation.

Csaba Szepesvári
Bandit Algorithms: What, Why and How?
Decision making in the face of uncertainty is a significant challenge in machine learning. Which drugs should a patient receive? How should I
allocate my study time between courses? Which version of a website will generate the most revenue? What move should be considered next when
playing chess/go? All of these questions can be expressed in the multi-armed bandit framework where a learning agent sequentially takes
actions, observes rewards and aims to maximize the total reward over a period of time.
The key challenge in all these problems is to carefully
balance exploration and exploitation. The framework is now very popular, used in practice by big companies, and growing fast. In this talk I will give a concise, illustrated overview of the challenges involved, explain the most important algorithmic and statistical ideas and discuss practical issues such as scaling bandits to large environments, or keeping exploration under control while avoiding suboptimality.