Machine Learning Seminar #3 - Probabilistic Programming
In this series of seminars, we are diving deeper into understanding, approaching and working with Machine Learning algorithms from different perspectives.
These events are not a lectures, but rather discussions that aim to expand the know-how and the understanding of machine learning.
It is known that the best way to learn and understand something fully, is to teach it to others. Hence, this is an opportunity for you to 'show-off' what you have learnt while at the same time deepening your knowledge in the field by teaching and explaining it, from your point of view, to the other members in the group.
Yet, this is not a competition. Gaps in the material can and should be filled by other members in the group. We're here to learn from each other - without judging.
Our topic this evening is Probabilistic Programming. Probabilistic programming is a programming method where the statistical models are separated from the code.
It is a hot topic that is getting more and more attention. On October 2018, the first conference about the topic was held - https://probprog.cc/.
There are more and more languages and frameworks to support it: Infer.NET (C#), Turing.jl (Julia), Stan (C++), as well as different implementations for PyTorch (Pyro) and TensorFlow (Edward) on Python, and more.
The seminar format works best if you come prepared. Please check the reading list below and bring your own insights, questions, and perplexities to the table!
## Recommended reading list:
# Background - Introduction to Probabilistic Programming:
Two shorter overviews:
# Two key-notes from the conference (video):
Probabilistic Machine Learning: From theory to industrial impact - Zoubin Ghahramani
Black Box Variational Inference - David Blei
As always, if you have more sources, please share them in the comments or the discussions/forum section of the meetup.
We look forward to seeing you!