Laplace’s Demon: A Seminar Series about Bayesian Machine Learning at Scale
Machine learning is changing the world we live in at a break neck pace. From image recognition and generation, to the deployment of recommender systems, it seems to be breaking new ground constantly and influencing almost every aspect of our lives. In ths seminar series we ask distinguished speakers to comment on what role Bayesian statistics and Bayesian machine learning have in this rapidly changing landscape. Do we need to optimally process information or borrow strength in the big data era? Are philosophical concepts such as coherence and the likelihood principle relevant when you are running a large scale recommender system? Are variational approximations, MCMC or EP appropriate in a production environment? Can I use the propensity score and call myself a Bayesian? How can I elicit a prior over a massive dataset? Is Bayes a reasonable theory of how to be perfect but a hopeless theory of how to be good? Do we need Bayes when we can just A/B test? What combinations of pragmatism and idealism can be used to deploy Bayesian machine learning in a large scale live system? We ask Bayesian believers, Bayesian pragmatists and Bayesian sceptics to comment on all of these subjects and more.
The audience is machine learning practitioners and statisticians from academia and industry.
To stay informed follow us on twitter, or we have a Google Group for general announcements and discussions related to the seminar series. Join the group here.
We have great speakers ove the whole year so please check out the full schedule below.
The registration link will allow you to see the time of the event in your timezone.
If you are interested in the seminar series as a whole then please join our list.
You should register individually for each seminar.