
Computation, Bayesian Model Selection, Interactive Articles
Machine Learning Street Talk (MLST)
00:00
Navigating Bayesian Complexity in Machine Learning
This chapter explores the intricacies of incorporating priors in machine learning, with a focus on neural networks and probabilistic optimization. It discusses the limitations of existing methodologies like maximum likelihood estimation and contrasts them with Bayesian model selection, emphasizing the benefits of treating parameters as distributions. The conversation highlights the potential of Bayesian methods to improve model performance and complexity control, paving the way for more effective model selection and interpretability.
Transcript
Play full episode