We'll focus on probabilistic models and their relation to machine learning and deep learning.

Brush up by checking out "Probabilistic Programming & Bayesian Methods for Hackers", Cam Davidson-Pilon http://nbviewer.jupyter.org/github/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/blob/master/Prologue/Prologue.ipynb

We're back to our discussion format so there will be no presentation this month.

Our topics will be:

1) Chapter 5 of "Bayesian Methods for Hackers" https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers Please take a look at it. It's perfectly OK if you are just starting the book. Come with your questions and we will discuss them.

2) "Sorry ARIMA, but I’m Going Bayesian" https://multithreaded.stitchfix.com/blog/2016/04/21/forget-arima/ I haven't dug in to this post but this is a perfect chance to figure out what the author is trying to say. The code in the article is in R ... why not try to reimplement it in your favorite language.

Note the doors will be locked. Please try to arrive by 6:15 and reach me here or on twitter, @JulioBarros, if you have any questions or need to be let in. Thanks.

As you know this is more of group discussion meetup where we get together to discuss what we are learning, interested in, stumped by, curious about, etc...

This month I propose we talk about Bayesian Deep Learning and study the materials from Yarin Gal's presentation at MLSS Moscow 2019 http://bdl101.ml

There wont be a presentation. Instead I encourage you to read over the materials, try out some experiments and come share your findings and experiences. I have lots of questions about when/where it is useful and what kind of future it may have. I'm looking forward to hearing your questions and thoughts.

All levels of experience and backgrounds are welcome so don't be shy. We all learn from each other.

Also, if you have ideas for topics you'd like to see in future meetings please let me know. Thanks.