These are a few textbooks that may be useful to research students at the Wellcome Centre for Human Neuroimaging. Currently, the focus is on nerdy stuff.
- Theoretical Neuroscience: Computational and Mathematical Modeling (pdf). Peter Dayan & L. F. Abbot. MIT Press (2001).
- Pattern Recognition and Machine Learning (pdf). Christopher M Bishop. Springer (2006).
- Probabilistic Machine Learning: An Introduction (pdf). Kevin P. Murphy. MIT Press (2022).
- Probabilistic Machine Learning: Advanced Topics (pdf). Kevin Patrick Murphy. MIT Press (2023).
- Probability Theory: The Logic of Science (pdf). Edwin T. Jaynes. Cambridge University Press (2003).
- Gaussian Processes for Machine Learning (pdf). Christopher K. Williams & Carl Edward Rasmussen. MIT press (2006).
- Deep Learning (unofficial link). Ian Goodfellow, Yoshua Bengio & Aaron Courville. MIT Press (2016).
- Dive Into Deep Learning (pdf). Aston Zhang, Zachary C. Lipton, Mu Li, & Alexander J Smola. arXiv preprint arXiv:2106.11342 (2021).
- Deep Learning: Foundations and Concepts. Christopher M Bishop & Hugh Bishop. Springer. 2023.
- Imaging-Genetics. Andre Altmann & Marco Lorenzi book chapter (2019).
- Numerical Recipes in C (Second Edition). Brian P Flannery, William H Press, Saul A Teukolsky & William Vetterling. Cambridge University Press (1992).
- The Matrix Cookbook (pdf). Kaare Brandt Petersen, Michael Syskind Pedersen & others. Technical University of Denmark 7(15) (2008).
- Statistics Cookbook. Copyright c Matthias Vallentin (http://statistics.zone/).