Issue #79
November 29, 2020
- 1. A Helping of Science With Your Thanksgiving Dinner [nytimes.com]
- 2. SQLite as a document database [dgl.cx]
- 3. Spatial Computing Could Be the Next Big Thing [scientificamerican.com]
- 4. How Misinformation ‘Superspreaders’ Seed False Election Theories [nytimes.com]
- 5. Landmark Papers in Machine Learning [github.com/daturkel]
- 6. Maximum Likelihood (ML) vs. REML [towardsdatascience.com]
- 7. Learning Causal Models [medium.com/@sgrimbly]
- 8. Interpretability in Machine Learning: An Overview [thegradient.pub]
- • 1.2 - Simpson's Paradox
- • 1.3 - Probability Theory and Statistics
- • 1.4 - Graphs
- • 1.5 - Structural Causal Models
- • 2.2 - Chains and Forks
- • 2.3 - Colliders
- • 2.4 - d-separation
- • 2.5 - Model Testing and Causal Search
- • 3.1 - Interventions
- • 3.2 - The Adjustment Formula
- • 3.3 - Backdoor Criterion
- • 3.4 - Front-door Criterion
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