Resources

Recommended textbooks

Neural Data science. Scott Linderman has written an excellent open source online textbook on Machine Learning Methods for Neural Data Analysis. Highly recommended for a thorough overview of the core challenges involved in analyzing neural data and computational approaches to solving them.

Computational Neuroscience. Dayan and Abbott’s Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems is still the classic introduction to modeling the brain.

Computational Cognition. Tom Griffiths, Nick Chater, and Josh Tenenbaum recently released a fantastic textbook covering Bayesian approaches to studying cognition with Bayesian Models of Cognition: Reverse Engineering the Mind.

General Neuroscience. Principles of Neural Science by Eric Kandel et al. The primary neurobiology reference, covering approaches to understanding the brain from genes and molecules up through cell types, circuits, behavior, and higher cognition.

General Machine Learning

• Pattern Recognition and Machine Learning by Chris Bishop is the bible for learning ML (or at least it was back when I was a PhD student!). Develops the Bayesian approach to ML from the outset.

• Gaussian Process for Machine Learning, a beautiful book on nonparametric approaches to regression and classification.

Causality. Causal Inference in Statistics: A Primer from Pearl et al is a short but extraordinarily illuminating introduction to causal inference, graphical models, the do-calculus, and counterfactuals. Highly recommended for those who tire of mere correlations!