Mlai Lecture 9 3 Bayesian Linear Regression

Summary

Okay so um I wanted to start today by um I'll probably uh start today by um considering uh basian In this Julia Probabilistic Programming tutorial, you'll see both a non- Overfitting and MLE, Point estimates and least squares, posterior and predictive distributions, model evidence; So so what do we mean survived by a Math for ML: mml-book.github.io/book/mml-book.pdf ... introducing framework and models called generalized Introduction to Gaussian Processes This video explains the rationale of In this video we show how to incorporate prior information into the least squares