Machine Learning
Table of Contents
- 1. Machine Learning
- 1.1. Recursos generales
- 1.2. Conferencias
- 1.3. ML Wiki
- 1.4. Machine Learning Computing alternative architectures
- 1.5. Regression
- 1.6. Neural Networks
- 1.7. Machine Learning & Physics
- 1.8. MLU-Explain
- 1.9. Interpretable Machine Learning
- 1.10. Ver “Carnegie Mellon University Deep Learning” en YouTube
- 1.11. Robert Miles - YouTube → Explaninable AI, AI Safety
- 1.12. Interpretable Deep Learning for New Physics Discovery - YouTube
1. Machine Learning
1.1. Recursos generales
1.2. Conferencias
- International Conference on Machine Learning - Wikipedia
- Conference on Neural Information Processing Systems - Wikipedia
- AAAI Conference on Artificial Intelligence - Wikipedia
- International Conference on Learning Representations - Wikipedia
- De aqhí se pueden sacar algunas referencias más de los papers que han publicado en la ICML burkholz
1.3. ML Wiki
1.4. Machine Learning Computing alternative architectures
1.5. Regression
1.5.1. Blinder–Oaxaca decomposition - Wikipedia
https://en.wikipedia.org/wiki/Blinder%E2%80%93Oaxaca_decomposition
In a regression
y1 = β1*X1 + μ1
y2 = β2*X2 + μ2
μ is error
b1, b2 estimates of β1, β2
mean(y1) - mean(y2) = b1*mean(X1) - b2*mean(X2) = b1*(mean(X1) - mean(X2)) + mean(X2)*(b1 - b2) = b2*(mean(X2) - mean(X1)) + mean(X1)*(b2 - b1)
- First term: the impact of between-group differences in the explanatory variables X, evaluated using the coefficients for group 1/2.
- Second term: differential not explained by these differences in observed characteristics X.
1.6.
1.8. MLU-Explain
https://mlu-explain.github.io/
Machine Learning University (MLU) is an education initiative from Amazon designed to teach machine learning theory and practical application.