Authors :
Jasmin Praful Bharadiya
Volume/Issue :
Volume 8 - 2023, Issue 5 - May
Google Scholar :
https://bit.ly/3TmGbDi
Scribd :
https://t.ly/aHQt
DOI :
https://doi.org/10.5281/zenodo.8020825
Abstract :
Bayesian machine learning is a subfield of
machine learning that incorporates Bayesian principles
and probabilistic models into the learning process. It
provides a principled framework for modeling
uncertainty, making predictions, and updating beliefs
based on observed data. This review article aims to
provide an overview of Bayesian machine learning,
discussing its foundational concepts, algorithms, and
applications. We explore key topics such as Bayesian
inference, probabilistic graphical models, Bayesian neural
networks, variational inference, Markov chain Monte
Carlo methods, and Bayesian optimization. Additionally,
we highlight the advantages and challenges of Bayesian
machine learning, discuss its application in various
domains, and identify future research directions. Deep
learning is a form of machine learning for nonlinear high
dimensional pattern matching and prediction. By taking a
Bayesian probabilistic perspective, we provide a number
of insights into more efficient algorithms for optimisation
and hyper-parameter tuning. Traditional highdimensional data reduction techniques, such as principal
component analysis (PCA), partial least squares (PLS),
reduced rank regression (RRR), projection pursuit
regression (PPR) are all shown to be shallow learners.
Their deep learning counterparts exploit multiple deep
layers of data reduction which provide predictive
performance gains. Stochastic gradient descent (SGD)
training optimisation and Dropout (DO) regularization
provide estimation and variable selection. Bayesian
regularization is central to finding weights and connections
in networks to optimize the predictive bias-variance tradeoff. To illustrate our methodology, we provide an analysis
of international bookings on Airbnb. Finally, we conclude
with directions for future research
Keywords :
Deep Learning, Machine Learning, Artificial Intelligence, Bayesian Hierarchical Models, Marginal Likelihood, Pattern Matching and Tensor flow.
Bayesian machine learning is a subfield of
machine learning that incorporates Bayesian principles
and probabilistic models into the learning process. It
provides a principled framework for modeling
uncertainty, making predictions, and updating beliefs
based on observed data. This review article aims to
provide an overview of Bayesian machine learning,
discussing its foundational concepts, algorithms, and
applications. We explore key topics such as Bayesian
inference, probabilistic graphical models, Bayesian neural
networks, variational inference, Markov chain Monte
Carlo methods, and Bayesian optimization. Additionally,
we highlight the advantages and challenges of Bayesian
machine learning, discuss its application in various
domains, and identify future research directions. Deep
learning is a form of machine learning for nonlinear high
dimensional pattern matching and prediction. By taking a
Bayesian probabilistic perspective, we provide a number
of insights into more efficient algorithms for optimisation
and hyper-parameter tuning. Traditional highdimensional data reduction techniques, such as principal
component analysis (PCA), partial least squares (PLS),
reduced rank regression (RRR), projection pursuit
regression (PPR) are all shown to be shallow learners.
Their deep learning counterparts exploit multiple deep
layers of data reduction which provide predictive
performance gains. Stochastic gradient descent (SGD)
training optimisation and Dropout (DO) regularization
provide estimation and variable selection. Bayesian
regularization is central to finding weights and connections
in networks to optimize the predictive bias-variance tradeoff. To illustrate our methodology, we provide an analysis
of international bookings on Airbnb. Finally, we conclude
with directions for future research
Keywords :
Deep Learning, Machine Learning, Artificial Intelligence, Bayesian Hierarchical Models, Marginal Likelihood, Pattern Matching and Tensor flow.
