Causal Inference
This is the eighth post on the series we work our way through “Causal Inference In Statistics” a nice Primer co-authored by Judea Pearl himself.
ISSUE #91
A weekly newsletter with the latest developments in Data Science and Machine Learning and Artificial Intelligence.
Feb 21, 2021
Dear friends,
Welcome to the Feb 21st issue of the Sunday Briefing.
This week we are taking a break from blogging but you can catch up on our recent posts. Over at Graphs For Data Science the latest post is: Word Networks for Language Generation. You should Subscribe to G4Sci to make sure you never miss a post!
3.7 — Mediation is the latest post in the Causality series while Competing CoVID-19 Strains is the most recent of the Epidemiology series. As always you can find the code in the Epidemiology and Causality GitHub repos, respectively.
ISSUE #90
A weekly newsletter with the latest developments in Data Science and Machine Learning and Artificial Intelligence.
Feb 14, 2021
Dear friends,
Welcome to the Valentine’s day issue of the Sunday Briefing.
This week we have two new blog posts. Over at Graphs For Data Science we just published: Word Networks for Language Generation where we explore the English Language Network and how can easily build a predictive text systems like the one you have on your smartphone. You should Subscribe to G4Sci to make sure you never miss a post!
We’ve also resumed the Causality blog series with a post covering Section 3.7 — Mediation and the procedure to compute causal effects when in the presence of mediating…
Causal Inference
This is the fifteenth post on the series we work our way through “Causal Inference In Statistics” a nice Primer co-authored by Judea Pearl himself.
ISSUE #89
A weekly newsletter with the latest developments in Data Science and Machine Learning and Artificial Intelligence.
Feb 07, 2021
Dear friends,
Welcome to the 89th issue of the Sunday Briefing.
This week we’re on hiatus from blogging. While we work on the next posts, you have some extra time to catch up on our most recent post over at Graphs For Data Science: Understanding DBSCAN and K-NN with Random Geometric Graphs where we explore the connections between Random Geometric Graphs and the K-Nearest Neighbor and DBSCAN machine learning algorithms. You should Subscribe to G4Sci to make sure you never miss a post!
You can catch up on our recent posts on Competing CoVID-19 Strains and Inverse Probability Weighing…
ISSUE #88
A weekly newsletter with the latest developments in Data Science and Machine Learning and Artificial Intelligence.
Jan 31, 2021
Dear friends,
Welcome to the Jan 31st issue of the Sunday Briefing.
This week we are happy to announce the most recent post over at Graphs For Data Science: Understanding DBSCAN and K-NN with Random Geometric Graphs. In it we explore the connections between Random Geometric Graphs and the K-Nearest Neighbor and DBSCAN machine learning algorithms. You should Subscribe to G4Sci to make sure you never miss a post!
You can catch up on our recent posts on Competing CoVID-19 Strains and Inverse Probability Weighing and as always you can find the code in the Epidemiology and Causality GitHub…
ISSUE #87
A weekly newsletter with the latest developments in Data Science and Machine Learning and Artificial Intelligence.
Jan 24, 2021
Dear friends,
Welcome to the 87th issue of the Sunday Briefing.
This week we are taking a break from blogging, but you can catch up on our recent posts on Competing CoVID-19 Strains and Inverse Probability Weighing and as always you can find the code in the Epidemiology and Causality GitHub repos, respectively.
The second ever post over at Graphs For Data Science is also available: Graphs 101 — A deep look at the airline transportation network. …
ISSUE #86
A weekly newsletter with the latest developments in Data Science and Machine Learning and Artificial Intelligence.
Jan 17, 2021
Dear friends,
Welcome to the 86th issue of the Sunday Briefing.
This week we have a double blogging feature. We just published the latest post on the Epidemic Modeling series, ‘Competing CoVID-19 Strains’ where we look at various scenarios of how the introduction of a new more virulent strain (like the UK one) might play out. As always you can find the code in the Epidemiology GitHub.
The second ever post over at Graphs For Data Science is now out: Graphs 101 — A deep look at the airline transportation network. In it we introduce some fundamental graph concepts…
Epidemiology 105
Welcome to the first 2021 post of the “Epidemic Modeling” series where I aim to guide you through the mathematical and conceptual details underlying epidemic modeling with a focus on what’s most relevant to the current CoVID-19 pandemic.
While I strive to make each post self contained, this post does build on previous ones that you might want to go back to the first one before getting started:
You can find the complete list of posts in this series at the bottom.
Each post also comes with a Jupyter…
ISSUE #85
A weekly newsletter with the latest developments in Data Science and Machine Learning and Artificial Intelligence.
Jan 10, 2021
Dear friends,
Welcome to the 85th issue of the Sunday Briefing.
This week we continue our hiatus from blogging about Causality and Epidemic Modeling. In the meantime, you can catch up on the Causality Series with our more recent post detailing Inverse Probability Weighing as a technique to facilitate the calculation of the effects of interventions. As usual don’t forget to checkout Causality GitHub repository. You can also get up to date wiht the Epidemiology series our recent post ‘CoVID-19: Forecasting the death toll’ and the code in the Epidemiology GitHub.
Bruno Gonçalves
Data Science, Machine Learning, Human Behavior
