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 #101
A weekly newsletter with the latest developments in Data Science and Machine Learning and Artificial Intelligence.
May 2, 2021
Dear friends,
Welcome to the 101st issue of the Sunday Briefing.
This week we’re on hiatus from blogging but you can catch up on the latest post over at G4Sci: Graph Components: Strongly and Weakly Connected Components an overview of algorithms to identify connected components and how they can be used to better understand a real world communication network. You should Subscribe to G4Sci to make sure you never miss a post!
Over at Medium, Competing CoVID-19 Strains is the most recent post on the Epidemiology series and Mediation is the latest for the Causality series while we continue…
ISSUE #100
A weekly newsletter with the latest developments in Data Science and Machine Learning and Artificial Intelligence.
Apr 25, 2021
Dear friends,
Welcome to the 100th issue of the Sunday Briefing. Can you believe we’ve been doing this for almost two years!
This week we have a new post over at G4Sci: Graph Components: Strongly and Weakly Connected Components an overview of algorithms to identify connected components and how they can be used to better understand a real world communication network. You should Subscribe to G4Sci to make sure you never miss a post!
Over at Medium, Competing CoVID-19 Strains is the most recent post on the Epidemiology series and Mediation is the latest for the Causality series while…
ISSUE #99
A weekly newsletter with the latest developments in Data Science and Machine Learning and Artificial Intelligence.
Apr 18, 2021
Dear friends,
Welcome to the 99th issue of the Sunday Briefing.
This week we’re on hiatus from blogging but you can catch the latest post is now out over at G4Sci: Node Centrality: Degree, Closeness, and Betweenness Centrality, an overview of centrality algorithms and how they can be used to identify the most important nodes in a network. You should Subscribe to G4Sci to make sure you never miss a post!
Over at Medium, Competing CoVID-19 Strains is the most recent post on the Epidemiology series and Mediation is the latest for the Causality series while we continue to…
ISSUE #98
A weekly newsletter with the latest developments in Data Science and Machine Learning and Artificial Intelligence.
Apr 11, 2021
Dear friends,
Welcome to the 98th issue of the Sunday Briefing.
The latest post is now out over at G4Sci: Node Centrality: Degree, Closeness, and Betweenness Centrality, an overview of centrality algorithms and how they can be used to identify the most important nodes in a network. You should Subscribe to G4Sci to make sure you never miss a post!
Over at Medium, Competing CoVID-19 Strains is the most recent post on the Epidemiology series and Mediation is the latest for the Causality series while we continue to work on the particularly long section 3.8 of the Primer. …
ISSUE #97
A weekly newsletter with the latest developments in Data Science and Machine Learning and Artificial Intelligence.
Apr 04, 2021
Dear friends,
Welcome to the Easter weekend issue of the Sunday Briefing.
This week we’re on a holiday hiatus from blogging, but you can catch up on the latest post over at G4Sci: Graph Embeddings 101: From word2vec to node2vec, and beyond, an overview of how node2vec uses random walks to turn word2vec into a graph embedding algorithm. You should Subscribe to G4Sci to make sure you never miss a post!
Over at Medium, Competing CoVID-19 Strains is the most recent post on the Epidemiology series and Mediation is the latest for the Causality series while we continue to…
ISSUE #96
A weekly newsletter with the latest developments in Data Science and Machine Learning and Artificial Intelligence.
Mar 28, 2021
Dear friends,
Welcome to the Passover weekend issue of the Sunday Briefing.
This week we’re happy to announce the latest post over at G4Sci: Graph Embeddings 101: From word2vec to node2vec, and beyond, an overview of how node2vec uses random walks to turn word2vec into a graph embedding algorithm. You should Subscribe to G4Sci to make sure you never miss a post!
Over at Medium, Competing CoVID-19 Strains is the most recent post on the Epidemiology series and Mediation is the latest for the Causality series while we continue to work on the particularly long section 3.8 of the…
ISSUE #95
A weekly newsletter with the latest developments in Data Science and Machine Learning and Artificial Intelligence.
Mar 21, 2021
Dear friends,
Welcome to the 95th issue of the Sunday Briefing.
This week we’re on hiatus from blogging but you can catch up on the latest post over at G4Sci: Structural Causal Models: Reasoning with DAGs, a quick overview of how DAGs are used in Causal Analysis. You should Subscribe to G4Sci to make sure you never miss a post!
Over at Medium, Competing CoVID-19 Strains is the most recent post on the Epidemiology series and Mediation is the latest for the Causality series while we continue to work on the particularly long section 3.8 of the Primer. …
Hands-on Tutorials
From distance matrices to graphs
In this post, we explore in more detail the specific properties of spatial networks and use them to gain some insight into two popular Machine Learning algorithms, k-Nearest Neighbors and DBSCAN.
ISSUE #94
A weekly newsletter with the latest developments in Data Science and Machine Learning and Artificial Intelligence.
Mar 14, 2021
Dear friends,
Welcome to the Pi day issue of the Sunday Briefing.
This week we’re happy to announce the latest post over at G4Sci: Structural Causal Models: Reasoning with DAGs, a quick overview of how DAGs are used in Causal Analysis. You should Subscribe to G4Sci to make sure you never miss a post! We’d also like to remind that we have the Network and Graph Algorithms webinar coming up on March 9. There’s just a couple of slots left so be sure to sign up!
Over at Medium, Mediation is the latest post in the Causality series while…
Bruno Gonçalves
Data Science, Machine Learning, Human Behavior
