Plethora of knowledge (papers, books, videos etc) in my journey to learn bayesian methods in machine learning
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Machine Learning A Probabilistic Perspective (A good start to learn probabilistic machine learning, the first two introductory chapters are pretty good for starter) - Kevin P. Murphy
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Statistical Rethinking - Richard McElreath | course | numpyro implementation | (seems good read later)
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Surrogates - Robert B. Gramacy
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Bayesian Optimization Book - Roman Garnett
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Course Notes for Bayesian Models for Machine Learning (A must read note, the formula derivations are nice) - Columbia University Fall 2016
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Variational Inference: A Review for Statisticians - Blei et al
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An Introduction to Variational Methodsfor Graphical Models - Jordan et al
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Advanced methods of variational inference - Deep Bayes 2018 - Max Welling
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Matrix Cook Book (many usefull facts on linear algebra and beyond)
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Deep Probabilistic Modelling with with Gaussian Processes - Neil D. Lawrence
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Bayesian workflow - Gelman et,al.
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Neil Lawrence's Talks - Neil D. Lawrence
- Bayesian Learning for Neural Networks - Neal, Radford M.
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Uncertainty in Deep Learning (PhD Thesis) - Yarin Gal
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Dropout as a Bayesian Approximation:Representing Model Uncertainty in Deep Learning - Yarin Gal
note that this method is little controversial following Ian Osband note on NIPS 2016 workshop. more details on r/machinelearning
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Gaussian Processes for Machine Learning - Carl Edward Rasmussen. short intro : link. video lecture : link
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Sparse GP using pseudo Input - Snelson & Ghahramani
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Scalable Gaussian process inference using variational methods - Matthews Thesis. (good resources to learn variational GP)
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A Practical Guide to Gaussian Processes - Deisenroth, Luo, Van der Wilk
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A Visual Exploration of Gaussian Processes - Görtler et al
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A Tutorial on Bayesian OptimizationPeter - I. Frazier
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Taking the Human Out of the Loop:A Review of Bayesian Optimization Nando Defreitas . more comprehensive than above
- The Multi-Armed Bandit Problem and Its Solutions -Lilian Weng
- Bayesian Method for Machine Learning - National Research University Higher School of Economics (Comprehensive, compact and intuitive course on bayesian method for machine learning, this is good start for getting basic intuition on bayesian methods)
- Deep|Bayes 2019 material and code: github video: youtube
- betanalpha.github.io - Michael Betancourt's blog
- Notes on machine learning - Peter Roelants's blog (have good notes on GP, Multivariate Gaussian and Multi-Armed bandit)
- Knowledge Gradient Visualized - Louis Tiao (Nice visualization to understand KG)
- Optimal Transport and Wassertein Distance - Larry Wasserman
- Understanding ELBO - Xitong Yang
- More on Multivariate Gaussians - Chuong B. Do
- Gaussian Densities - Tony E. Smith
- Gumble Distribution - mrahtz
- Expected Improvement Derivation - Al-Dujaili
- Math for Machine Learning - Hal Daumé III