Having witnessed a rapid evolution of Data Science as a discipline over the past 15+ years, the breathtaking advances in capability keep coming as we have been in a special era of time.
The below is but a woefully incomplete yet hopefully useful subset of literature resources to help stay current.
- Reinforcement Learning: An Introduction. Richard S. Sutton and Andrew G. Barto
- Berkeley Deep Reinforcement Learning Course CS285
- Deep Reinforcement Learning
- Reinforcement Learning and Stochastic Optimization: A unified framework for sequential decisions. Powell
- Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology
- Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges. Bronstein
- Neural Message Passing for Quantum Chemistry
- Generalized Additive Models. Wood
- High-Dimensional Data Analysis with Low-Dimensional Models: Principles, Computation, and Applications. Wright
- Probabilistic Machine Learning. Kevin Murphy's 3 PML books
- Bayesian Data Analysis Third edition. Gelman
- Bayesian Modeling and Computation in Python and repo
- Bayes Cognitive Modeling. Lee & Wagenmakers
- Causal Inference in Statistics
- Confidence Intervals. Jaynes
- Statistical Rethinking. McElreath
- Time Series Analysis and it's Applications. Shumway & Stoffer
- Doing Bayesian Data Analysis in brms and the tidyverse. Kurz
- Applied longitudinal data analysis in brms and the tidyverse. Kurz
- Statistical Rethinking with brms, ggplot2, and the tidyverse. Kurz