Update notes.txt

doc/src/Bayesian/notes.txt

@@ -1,14 +1,23 @@
-A group of machine learning algorithms where the fits and optimizations are based on Bayesian statistics (Bayes’ Theorem) instead of traditional statistics and optimizations 
-Assume outputs can be described as distributions instead of as linear and nonlinear combinations of inputs and hyperparameters are fit using priors instead of being set by user
-Benefits: No hyperparameter tuning, no validation data set, produced uncertainties on predictions 
+Topics to conaider adding:
+
+1) words on Bayesian ML
+
+A group of machine learning algorithms where the fits and
+optimizations are based on Bayesian statistics (Bayes’ Theorem)
+instead of traditional statistics and optimizations Assume outputs can
+be described as distributions instead of as linear and nonlinear
+combinations of inputs and hyperparameters are fit using priors
+instead of being set by user Benefits: No hyperparameter tuning, no
+validation data set, produced uncertainties on predictions
 
 
+2) Discuss Kernel regression first and then add Bayesian Ridge regression
 Bayesian Ridge Regression
 Bayesian version of ridge regression (regularized linear regression)
 Finds parameters and hyperparameters using Gaussian distributions 
 Different results than ridge regression but does not depend on user-ser hyperparameter
 
-
+3) GP 
 Gaussian Processes
 Bayesian version of kernel ridge regression or support vector machines
 Similar to Bayesian ridge regression but uses the kernel trick to modify the inputs