Kernels and Gaussian Processes

Lecture slides:

• Linear Regression
• Example Prediction Problem
• Linear Model
• Loss Functions
• Squared-Error Loss
• Matrix Notation
• Minimising MSE
• Stationary Point
• Least Squares Solution
• Prediction
• Nonlinear Model
• Probabilistic Regression
• Noise Distribution
• Maximum Likelihood
• Estimate Uncertainty
• Likelihood

Author: Mark Grolami, University of Glasgow