ProbabilisticMachineLearningResources.md

Probabilistic Machine Learning Resources

Books

• Machine Learning: A Probabilistic Perspective, Kevin P. Murphy, 2012

• Probabilistic Machine Learning: An Introduction (Draft), Kevin P. Murphy, 2020

• The Design of Experiments, R.A. Fisher, Reprint 1974 (First edition 1935)

• Pattern Recognition and Machine Learning, Christopher M. Bishop, 2006

• The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation, Christian P. Robert, 2nd Edition, 2007

• Bayesian Data Analysis, 3rd edition, Andrew Gelman, John B. Carlin, Hal Stern, David Dunson, Aki Vehtari, Donald Rubin, 2021

• Baeysian Theory, Jose Bernardo, Adrian Smith, 2000

• Statistical Rethinking: A Bayesian Course with Examples of R and Stan, Richard McElreath, 2020

• Monte Carlo Statistical Methods, Christian P. Robert, George Cassela, 2nd Edition, 2004

• Experimental Design and Analysis, Howard J. Seltman, 2018

• Principles of Statistical Inference, D.R. Cox, 2006

• Probability and Measure, J.R. Norris, Lecture notes, U. of Cambridge

• Probability and Measure, Patrick Billingsley, Third Edition, U. of Chicago, 1995

• Probability and Random Processes, Geoffrey Grimmet, David Stirzaker, Third Edition, U. of Cambridge, U. of Oxford, 2001

• A First Course in Random Matrix Theory, Marc Potters, Jean-Philippe Bouchaud

• Introduction to Random Matrices: Theory and Pracice, Giacomo Livan, Marcel Novaes, Pierpaolo Vivo

• Dynamic Programming and Markov Processes, Ronald A. Howard, 1960, MIT Press

• Markov Processes: Theorems and Problems, E. Dynkin, A. Yushkevich, 1969

• An Introduction to the Theory of Point Processes: Vol I Elementary Theory and Methods, D.J. Daley, D. Vere-Jones, 2nd Edition, 2002

• The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation and Machine Learning, RY Rubinstein, DP Kroese, 2004

• Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, Bernhard Schoelkopf, Alexander J. Smola, MIT, 2002

articles

• Bayesian Data Analysis, 3rd Edition, Gelman, Carlin, Stern, Dunson, et al., 2021

• Inference from Observations to Simple Statistical Hypotheses, J. Grossman, PhD Thesis, University of Sydney, 2005

• Bayesian Analysis of Spatial Point Patterns, Thomas Leininger, Duke U., 2014

• A Bayesian Framework for Concept Learning, Joshua Tenenbaum, PhD Thesis, MIT, 1999

• Detecting Novel Associations for Large Datasets, D. Reshef, et al, 2011

• Generalization, similarity, and Bayesian inference, J. Tenenbaum, et al, 2001

• Hyperspacing and the Estimation of Information Theoretic Quantities, Learned-Miller, 2004

• On The Optimality of the Simple Bayesian Classifier under Zero-One Loss, Domingos, Pazzani, 1997

• Modeling Almost Periodicity In Point Processes, N. Shao, PhD Thesis, 2010

Markov Processes

• Dynamic Programming and Markov Processes, Ronald A. Howard, 1960

• Markov Processes Theorems and Problems, Evgenii Dynkin, 1969

• Reversibility and Stochastic Processes, F.P. Kelly, 1979

• Hidden Markov Models Lecture Notes, Ramon van Handel, 2008

MCMC

• A Tutorial on Markov Chain Monte-Carlo and Bayesian Modeling, Martin B. Haugh, Imperial College London, 2021

• Tutorial on Markov Chain Monte Carlo, Kenneth M. Hanson, Los Alamos National Laboratory, 2000

• Monte Carlo Statistical Methods, Christian P. Robert, George Cassela, 2nd Edition, 2004

• Creating animations with MCMC, Jan Krepl, online blog, 2024

  relate repo: https://github.com/ColCarroll/imcmc

Bayesian Optimization

• A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning, Eric Brochu, Vlad M. Cora and Nando Freitas, 2010

• Vanilla Bayesian Optimization Performs Great in High Dimensions, Carl Hvarfner, Erik O. Hellsten, Luigi Nardi, 2024

• Information-Theoretic Safe Bayesian Optimization, AG Bottero et al, TU Darmstadt, 2024

• Bayesian optimization and multi-armed bandits, Nando de Freitas, 2013, youtube lecture

• Pareto Front-Diverse Batch Multi-Objective Bayesian Optimization, A. Ahmadianshalchi et al, AAAI 2024

• Practical Bayesian Optimization, Danel J. Lizotte, U of Alberta, PhD thesis, 2008

• Taking the Human Out of the Loop: A Review of Bayesian Optimization, B. Shahriari et al, UBC, 2016

• Bayesian Optimization for Multi-objective Optimization and Multi-point Search, T. Wada et al, Kobe Steel Ltd, ISM, 2019

• Multi-objective Bayesian optimisation with preferences over the objectives, M. Abdolshah et al, NeurIPS 2019

• Max-value Entropy Search for Multi-Objective Bayesian Optimization, S. Belakaria et al, WSU, NeurIPS 2019

• Multi-objective Bayesian Optimization using Pareto-frontier Entropy, S. Suzuki et al, ICML 2020

• Diversity-Guided Multi-Objective Bayesian Optimization With Batch Evaluations, M. Lukovic et al, MIT CSAIL, NeurIPS 2020

  related github repo: https://github.com/yunshengtian/DGEMO

• Improved Max-value Entropy Search for Multi-objective Bayesian Optimization with Constraints, D. Fernandez-Sanchez et al, 2021

• Multi-Dimensional Bayesian Optimization over High-Dimensional Search Spaces, S. Daulton et al, U of Oxford, UAI 2022

• A New Acquisition Function for Multi-objective Bayesian Optimization: Correlated Probability of Improvement, K. Yang, UAS-Upper Austria, CSU Changsha, 2023

Theory of Random Matrices and their Applications

• Introduction to Random Matrices: Theory and Practice, G. Livan, M. Novaes, P. Vivo, 2017

• A First Course in Random Matrix Theory, Marc Potters and Jean-Philippe Bouchaud, 2021

• Can customer arrival rates be modelled by sine waves?, N. Chen et al, 2018

medium

• Understanding Diffusion Probabilistic Models (DPMs) with Joseph Rocca

• Probabilistic ML with Quantile Matching: an Example with Python with Davide Burba

• Use Frequency More Frequently with Daniel Warfield

• Change Point Detection — A Bayesian Approach with Everton Almeida

• Rubik’s cubes and Markov chains with Eduardo Teste

• Hidden Markov Models Simplified with Sanjay Dorairaj

• Statistical Learning Theory: Hoeffding’s Inequality Derivation & Simulation with Andrew Rothman

• The Markov and the Bienaymé–Chebyshev Inequalities with Sachin Date

• Probabilistic Programming with Differential Equation Models, Laura Mansfield, Aug 19, 2019

online videos, books, blogs and tutorials

Theory of Random Matrices

Random Matrices: Theory and Practice - Lecture 1, P. Vivo, King's College, London

Random Matrices: Theory and Practice - Lecture 2, P. Vivo, King's College, London

Random Matrices: Theory and Practice - Lecture 3, P. Vivo, King's College, London

Random Matrices: Theory and Practice - Lecture 4, P. Vivo, King's College, London

Random Matrices: Theory and Practice - Lecture 5, P. Vivo, King's College, London

Random Matrices: Theory and Practice - Lecture 6, P. Vivo, King's College, London

Random Matrices: Theory and Practice - Lecture 7, P. Vivo, King's College, London

Random Matrices: Theory and Practice - Lecture 8, P. Vivo, King's College, London

Random Matrices: Theory and Practice - Lecture 9, P. Vivo, King's College, London

Bayesian Methods and Bayesian Learning

Bayesian Methods for Hackers, Cam Davidson Pilon, online book on github

Bayesian Learning for Linear Models, Nando de Freitas (part 1), BCSC 540, UBC Jan 24, 2013

Bayesian Learning for Linear Models, Nando de Freitas (part 2), BCSC 540, UBC Jan 24, 2013

Gaussian Processes

Introduction to Gaussian Processes and Gaussian Process Regression, Nando de Freitas, CBSC 540, UBC Jan 31, 2013

Active Learning with Gaussian Processes, Nando de Freitas, BCSC 540, UBC Feb 05, 2013

Bayesian Optimization

Bayesian optimization, Thompson sampling and multi-armed bandits. Applications to algorithm configuration, intelligent user interfaces, advertising, control and other decision problems, Nando de Freitas, CBSC 540, UBC Feb 07, 2013

Bayesian Optimization with Extensions, Applications, and other sundry items, Matthew W. Hoffman, DeepMind, Aug 6, 2018 , UAI 2018

Bayesian Optimization, Marc Deisenroth, COMP0168 (2020/21)

Bayesian Optimization, Roman Garnett, Tübingen Machine Learning, Probabilistic Numerics Spring School 2023 in Tübingen, March 2023

Decision Trees and Random Forests

Decision Trees for Classification, Nando de Freitas, CBSC 540, UBC, Feb 12, 2013

Random forests, aka decision forests, and ensemble methods, Nando de Freitas, CBSC 540, UBC, Feb 14, 2013

Applications of random forests: kinect, object detection and regression, Nando de Freitas, CBSC 540, UBC, Feb 26, 2013

MCMC

Importance sampling and Markov chain Monte Carlo (MCMC). Application to logistic regression, Nando de Freitas, CBSC 540, UBC, March 19, 2013

Metropolis-Hastings algorithm. Application to logistic regression, Nando de Freitas, CBSC 540, UBC, March 21, 2013