The Bayesian Approach to Machine Learning

Dr. Md Dilshad Ghani

Abstract: A branch of machine learning known as "Bayesian machine learning" applies probabilistic models and Bayesian concepts to the process of learning. It offers a moral framework for forecasting, revising beliefs, and modelling uncertainty in light of data. By going over its fundamental ideas, techniques, and applications, this review article seeks to give a general understanding of Bayesian machine learning. We examine important subjects like variational inference, Bayesian neural networks, Bayesian inference, probabilistic graphical models, Markov chain Monte Carlo techniques, and Bayesian optimization. Furthermore, we outline the benefits and difficulties of Bayesian machine learning, talk about its use in other fields, and suggest avenues for further study. A type of machine learning for nonlinear, high-dimensional pattern matching and prediction is called deep learning. We offer several insights into more effective optimization and hyper-parameter tuning algorithms by adopting a Bayesian probabilistic viewpoint. It has been demonstrated that conventional high-dimensional data reduction methods like projection pursuit regression (PPR), reduced rank regression (RRR), partial least squares (PLS), and principal component analysis (PCA) are shallow learners. In order to improve prediction performance, their deep learning counterparts take advantage of several deep layers of data reduction. Estimation and variable selection are provided by Dropout (DO) regularization and stochastic gradient descent (SGD) training optimization. Finding weights and connections in networks to maximize the predictive bias-variance tradeoff requires the use of Bayesian regularization. We present a study of Airbnb's foreign bookings to demonstrate our methodology. We wrap up by offering suggestions for further study.

Keywords: Deep Learning, Machine Learning, Artificial Intelligence, Bayesian Hierarchical Models, Marginal Likelihood, Pattern Matching and Tensor flow