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Body of Knowledge
Advanced Data Science

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Advanced Data Science

Overview
Point vs. probabilistic statements
Inference and learning
Least squares regression
Classification
Least squares autoencoders
k-means clustering
Shrinkage
Discriminant regression
Discriminant classification
Probabilistic graphical models
Maximum likelihood
Regularization and features selection
Bayesian
Mixed approach
Foundations of decision theory
Estimation risk assessment
Bias reduction
Functional bias reduction
Linear basis expansion
Quasi-linear adaptive basis
Adaptive networks
Gradient boosting
Kernel trick
Background
Shrinkage
Regularization
Ensemble learning
Credit default classification
Background
Fit and assessment
Logistic regression
Interactions
Encoding
Regularization
Trees
Gradient boosting
Cross-validation
Generalized probabilistic inference
Minimum relative entropy
Analytical implementation
Flexible probabilities implementation
Overview
Least squares dynamic models
Wiener-Kolmogorov filtering
Dynamic principal component
Probabilistic state space models
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