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K
SIMLES
Resources
Programming
1. Probability Models
Basics of probabilities
Random Variables
Lab 1.1
Moments
Sample Statistics
2. Inference
Estimators
Cookbook: MLE for Gutenberg-Richter Law
Cookbook: Precipitation return times for Hurricane Harvey
Lab 2.1
Lab 2.1
Quality of Estimators
Hypothesis Testing
Lab 2.2
3. Machine Learning
Logistic Regression - Binary
Logistic Regression - MultiNomial
Neural Nets for Classification - Multi Layer Perceptron
Neural Nets for Regresssion - Multi Layer Perceptron
Convolutional Neural Nets for Image Recognition
Repository
Open issue
Index
R
R
RFC
RFC 2616
,
[1]
RFC 5322
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[1]
