Gaussian Process Regression: Probabilistic Linear Regression

This lecture covers Probabilistic Linear Regression, focusing on Maximum Likelihood Estimation and Maximum a Posteriori Estimation. It then transitions to Gaussian Process Regression, explaining the kernel definition, hyperparameters tuning, and prediction away from data. The instructor emphasizes the importance of choosing the right kernel and hyperparameters for accurate predictions and discusses the trade-off between fit and complexity. The lecture concludes by highlighting the advantages of Gaussian Process Regression, such as accurate predictions and uncertainty estimation, while acknowledging its computational complexity.