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learn machine learning foundations
data, training, evaluation, and the core algorithms every other path builds on — the right place to begin.
curatedbeginner~4 weeks, part-time
machine learning foundations
the bedrock every ai/ml path stands on: how learning from data actually works, the core algorithms, and how to tell a good model from a lucky one.
4 modules · 12 resources · checkpoint per modulestay current
see the full digest →what's new in machine learning foundations
- Accelerating A/B-Tests with Counterfactual Estimation: Reducing Variance through Policy Overlapthis paper proposes using counterfactual estimation and policy overlap to reduce variance in a/b tests, thereby accelerating them. practitioners can leverage this to run online experiments more efficiently, achieving statistically significant results faster and reducing the cost of experimentation.
- The Adversarial Robustness of Sketching and Streaming Algorithmsthis paper investigates the adversarial robustness of sketching and streaming algorithms, which are vital for massive datasets. practitioners deploying these algorithms in sensitive or adversarial environments can understand their vulnerabilities and design more robust systems against data poisoning or manipulation.
- Deep Gaussian Processes on Directed Acyclic Graphsthis paper introduces deep gaussian processes (dgps) adapted for directed acyclic graphs (dags), which are common in real-world processes. practitioners working with structured data, like causal graphs or biological networks, can use this to model complex relationships with uncertainty quantification.
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