At this meeting we will have: - an intro/review of probability distributions and where to use them - a discussion on a real life case study - a discussion on chapters 1-3 of the book.

Come on out and learn, teach and discuss probabilistic programming.

This month we'll focus a bit more on the 'programming' part of Probabilisitic Programming. First we'll have:

Probabilistic programming from scratch

Real world observational data is always imperfect or incomplete in some way. Those limitations mean that what we learn from our data is somewhat uncertain. We want to fill in the blanks to the extent possible and be able to say how confident we are as we do this. This is inference. Probabilistic programming makes it easier to learn from data. Let’s see how to build a basic probabilistic programming system from scratch in Python to introduce Approximate Bayesian Computation (ABC), which is a specific, extremely simple algorithm to perform Bayesian inference.

Bio: David is a Business Analyst and Software Developer with six years of corporate experience in building databases, managing and analyzing large data sets, and optimizing systems and processes. Machine Learning enthusiast focused on Natural Language Processing (NLP) and visual recognition.

Then we'll introduce and discuss "An Introduction to Probabilistic Programming" https://arxiv.org/abs/1809.10756 and explore some of the concepts we've covered using Pyro http://pyro.ai

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.

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

Our topics will be:

1) Chapter 6: Getting our priorities straight 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) Anything else probabilistic we can think of.

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.