References#

BPRS18

Atilim Gunes Baydin, Barak A. Pearlmutter, Alexey Andreyevich Radul, and Jeffrey Mark Siskind. Automatic differentiation in machine learning: a survey. 2018. URL: https://arxiv.org/abs/1502.05767.

Bis06

Christopher M. Bishop. Pattern recognition and machine learning. Springer, 2006. ISBN 978-0-387-31073-2. URL: https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf.

Bol09

William M. Bolstad. Understanding Computational Bayesian Statistics. Wiley, 2009. ISBN 978-0-470-04609-8. URL: https://onlinelibrary.wiley.com/doi/book/10.1002/9780470567371.

BV04

Stephen P. Boyd and Lieven Vandenberghe. Convex optimization. Cambridge University Press, 2004. ISBN 978-0-521-83378-3. URL: https://web.stanford.edu/~boyd/cvxbook/.

BK19

Steven L. Brunton and J. Nathan Kutz. Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control. Cambridge University Press, 2019. URL: http://databookuw.com, doi:10.1017/9781108380690.

Duv14

David Duvenaud. Automatic model construction with gaussian processes. 2014. URL: https://www.cs.toronto.edu/~duvenaud/thesis.pdf.

GBC16

Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016. URL: http://www.deeplearningbook.org.

GJ23

Bernd Gärtner and Martin Jaggi. Optimization for machine learning. 2023. URL: epfml/OptML_course.

HR22

Moritz Hardt and Benjamin Recht. Patterns, predictions, and actions: Foundations of machine learning. Princeton University Press, 2022. URL: https://mlstory.org/.

Mac16

Dougal Maclaurin. Modeling, inference and optimization with composable differentiable procedures. 2016. URL: https://dougalmaclaurin.com/phd-thesis.pdf.

Mur22

Kevin P. Murphy. Probabilistic Machine Learning: An introduction. MIT Press, 2022. URL: http://probml.github.io/book1.

Ng22

Andrew Ng. Cs229 lecture notes. Running file URL: https://cs229.stanford.edu/main_notes.pdf, 2022. URL: https://cs229.stanford.edu/notes2022fall/main_notes.pdf.

NW06

Jorge Nocedal and Stephen J. Wright. Numerical optimization. Springer, 2006. ISBN 978-0-387-30303-1. URL: https://link.springer.com/book/10.1007/978-0-387-40065-5.

RW06

Carl Edward Rasmussen and Christopher KI Williams. Gaussian Processes for Machine Learning. MIT Press, 2006. URL: https://gaussianprocess.org/gpml/.

RC04

Christian P Robert and George Casella. Monte Carlo statistical methods. Springer, 2004. URL: https://link.springer.com/book/10.1007/978-1-4757-4145-2.

ZLLS21

Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola. Dive into deep learning. arXiv preprint arXiv:2106.11342, 2021. URL: https://d2l.ai/.