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1. Supervised learning
1.1. Generalized Linear Models
1.1.1. Bayesian Linear Regression
1.1.2. Bayesian Logistic regression
1.2. Gaussian Processes
1.2.1. Gaussian Process Regression (GPR)
1.2.2. Kernels for Gaussian Processes
1.2.2.1. References
1.3. Naive Bayes
1.3.1. Gaussian Naive Bayes
1.4. Neural network models (supervised)
1.4.1. Multi-layer Perceptron
1.4.2. Classification
2. Unsupervised learning
2.1. Gaussian mixture models
2.1.1. Gaussian Mixture
2.1.2. The Dirichlet Process
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