Topics in Statistics and Probability

Miguel Morales. Grokking Deep Reinforcement Learning. Manning.

Listed textbook (requirement not stated): R. S. Sutton and A. G. Barto. Reinforcement Learning: An Introduction, 2nd edition. MIT Press. http://incompleteideas.net/book/the-book-2nd.html

Reinforcement Learning: An Introduction, by R.S. Sutton and A.G. Barto, 2nd Edition, MIT Press.

W. John Braun and Duncan J. Murdoch. A First Course in Statistical Programming with R, 2nd edition. Cambridge University Press.

Listed textbook (requirement not stated): Douglas C. Montgomery. Design and Analysis of Experiments, 10th edition. John Wiley, 2020.

Reference (not explicitly required): S. R. Searle. Linear Models. John Wiley, 1971.

Reference (not explicitly required): C. C. Aggarwal. Neural Networks and Deep Learning: A Textbook, 2nd edition. Springer, 2023.

Reference (not explicitly required): C. M. Bishop and H. Bishop. Deep Learning: Foundations and Concepts. Springer, 2024. https://www.bishopbook.com/

Listed textbook (requirement not stated): G. James, D. Witten, T. Hastie and R. Tibshirani. An Introduction to Statistical Learning. Springer. https://www.statlearning.com/

Listed textbook (requirement not stated): J. Friedman, T. Hastie and R. Tibshirani. The Elements of Statistical Learning. Springer Series in Statistics. https://web.stanford.edu/~hastie/ElemStatLearn/

An introduction to statistical learning, by G. James, D Witten, T. Hastie and R. Tibshirani, Springer.

The elements of statistical learning, by J. Friedman, T. Hastie and R. Tibshirani, Springer series in statistics.

An introduction to statistical learning, by G. James, D Witten, T. Hastie and R. Tibshirani, Springer.

The elements of statistical learning, by J. Friedman, T. Hastie and R. Tibshirani, Springer series in statistics.

Neural networks and deep learning: a textbook, C.C. Aggarwal, 2nd Edition, Springer, 2023.

Deep learning: foundations and concepts, by C.M. Bishop and H. Bishop, Springer.

A. E. Kyprianou, Introductory Lectures on Fluctuations of Levy Processes with Application, 2nd edition, Springer.

A. E. Kyprianou, Gerber-Shiu Risk Theory, Springer.

A First Course in Bayesian Statistical Methods, by Peter D. Hoff, Springer.

Bayesian Data Analysis, by Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin, 3rd Edition, CRC Press.

An Introduction to Bayesian Analysis: Theory and Methods, by Jayanta K. Ghosh, Mohan Delampady, Tapas Samanta, Springer, 2009.

A First Course in Bayesian Statistical Methods, by Peter D. Hoff, Springer.

Bayesian Data Analysis, by Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin, 3rd Edition, CRC Press.

An Introduction to Bayesian Analysis: Theory and Methods, by Jayanta K. Ghosh, Mohan Delampady, Tapas Samanta, Springer, 2009.

Listed textbook (requirement not stated): Gregory F. Lawler. Introduction to Stochastic Processes, 2nd edition. Chapman & Hall/CRC, 2006.

Scheduled textbook sections: 1.1, 1.2, 1.4, 1.5, 2.1, 2.2, 2.4, 3.1–3.3, 4.1–4.3, 5.1–5.6, 8.1–8.8, 9.1–9.8.

An introduction to statistical learning, by G. James, D Witten, T. Hastie and R. Tibshirani, Springer.

The elements of statistical learning, by J. Friedman, T. Hastie and R. Tibshirani, Springer series in statistics.

An introduction to statistical learning, by G. James, D Witten, T. Hastie and R. Tibshirani, Springer.

The elements of statistical learning, by J. Friedman, T. Hastie and R. Tibshirani, Springer series in statistics.

Neural networks and deep learning: a textbook, C.C. Aggarwal, 2nd Edition, Springer, 2023.

Deep learning: foundations and concepts, by C.M. Bishop and H. Bishop, Springer.

Reinforcement Learning: An Introduction, by R.S. Sutton and A.G. Barto, 2nd Edition, MIT Press.

Grokking Deep Reinforcement Learning, by Miguel Morales, Manning Publications.

An introduction to statistical learning, by G. James, D Witten, T. Hastie and R. Tibshirani, Springer.

The elements of statistical learning, by J. Friedman, T. Hastie and R. Tibshirani, Springer series in statistics.

Neural networks and deep learning: a textbook, C.C. Aggarwal, 2nd Edition, Springer, 2023.

Deep learning: foundations and concepts, by C.M. Bishop and H. Bishop, Springer.

Reinforcement Learning: An Introduction, by R.S. Sutton and A.G. Barto, 2nd Edition, MIT Press.

Grokking Deep Reinforcement Learning, by Miguel Morales, Manning Publications.

Listed textbook (requirement not stated): R. A. Johnson and D. W. Wichern. Applied Multivariate Statistical Analysis, 6th edition. Pearson Prentice Hall, 2007.

Reference (not explicitly required): C. R. Rao. Linear Statistical Inference and Its Applications, 2nd edition. Wiley, 1973.

An introduction to statistical learning, by G. James, D Witten, T. Hastie and R. Tibshirani, Springer.

The elements of statistical learning, by J. Friedman, T. Hastie and R. Tibshirani, Springer series in statistics.

An introduction to statistical learning, by G. James, D Witten, T. Hastie and R. Tibshirani, Springer.

The elements of statistical learning, by J. Friedman, T. Hastie and R. Tibshirani, Springer series in statistics.

Reinforcement Learning: An Introduction, by R.S. Sutton and A.G. Barto, 2nd Edition, MIT Press.

An introduction to statistical learning, by G. James, D Witten, T. Hastie and R. Tibshirani, Springer.

The elements of statistical learning, by J. Friedman, T. Hastie and R. Tibshirani, Springer series in statistics.

Design and Analysis of Experiments, 10th Edition, by Douglas C. Montgomery (John Wiley, 2020).

Linear Models by S.R. Searle (John Wiley, 1971).

Reinforcement Learning: An Introduction, by R.S. Sutton and A.G. Barto, 2nd Edition, MIT Press.

An introduction to statistical learning, by G. James, D Witten, T. Hastie and R. Tibshirani, Springer.

The elements of statistical learning, by J. Friedman, T. Hastie and R. Tibshirani, Springer series in statistics.

An introduction to statistical learning, by G. James, D Witten, T. Hastie and R. Tibshirani, Springer.

The elements of statistical learning, by J. Friedman, T. Hastie and R. Tibshirani, Springer series in statistics.

Introduction to Stochastic Processes, 2nd Edition, by Gregory F. Lawler, Chapman & Hall/CRC (2006).

Applied Multivariate Statistical Analysis, 6th Edition, by R. A. Johnson and D. W. Wichern, Pearson Prentice Hall (2007).

Linear Statistical Inference and Its Applications, 2nd Edition, by C. R. Rao, Wiley (1973).

An introduction to statistical learning, by G. James, D Witten, T. Hastie and R. Tibshirani, Springer.

The elements of statistical learning, by J. Friedman, T. Hastie and R. Tibshirani, Springer series in statistics.

An introduction to statistical learning, by G. James, D Witten, T. Hastie and R. Tibshirani, Springer.

The elements of statistical learning, by J. Friedman, T. Hastie and R. Tibshirani, Springer series in statistics.

Design and Analysis of Experiments, 10th Edition, by Douglas C. Montgomery (John Wiley, 2020).

Linear Models by S.R. Searle (John Wiley, 1971).

Reinforcement Learning: An Introduction, by R.S. Sutton and A.G. Barto, 2nd Edition, MIT Press.

Applied Multivariate Statistical Analysis, 6th Edition, by R. A. Johnson and D. W. Wichern, Pearson Prentice Hall (2007).

Linear Statistical Inference and Its Applications, 2nd Edition, by C. R. Rao, Wiley (1973).

Reinforcement Learning: An Introduction, by R.S. Sutton and A.G. Barto, 2nd Edition, MIT Press.