Probabilistic theory, covariances, multivariate normal distributions, guassian processes.

Page List

• Fundamental Properties — basic rules for independence, expected values, covariance and conditional/marginal distributions.
• GP Regression — using guassian processes to learn from trained data and do inferencing on unknowns.
• Guassian Distributions — basic properties of guassian (normal) distributions.
• Guassian Processes — definition and introduction to guassian processes.
• Linear Regression — introducing a prior belief to the linear weights and using MAP to do estimation/prediction.
• Linear Regression - ML — linear regression via maximum likelihood and some simple assumptions about the system.
• Maximum Likelihood, Maximum A Posteriori and Bayesian — the holy trinity of parameter estimation and data prediction

Notes

• Great series of machine learning lectures put together by mathematical monk@youtube.