# Resources

Here is a collection of resources for Machine Learning and Computational Neuroscience, and some other things I find useful or interesting.

**Machine Learning**

#### Links

- website with useful resources on Gaussian Processes
- Tom Minka’s EP roadmap
- Blog on probabilistic numerics
- lecture slides for Gatsby Machine Learning courses
- Wainwright & Jordan’s review on Graphical Models, Exponential Families and Variational Inference
- Max Welling’s classnotes
- Arthur Gretton’s lecture notes on RKHS theory
- Orbanz’s lecture notes on Bayesian Nonparametrics
- Chris Olah’s blog with useful material on neural networks
- this visual exploration of Gaussian Processes
- Matrix Cookbook
- Matrix Reference Manual
- useful tool for matrix calculus

#### Books

- Gaussian Process Book
- Bishop’s Pattern Recognition and Machine Learning textbook
- Murphy’s Machine Learning textbook
- MacKay’s textbook and online lectures
- Sutton & Barto’s Reinforcement Learning Textbook
- Deep Learning textbook

**Computational Neuroscience**

#### Links

- Gatsby/SWC Systems and Theoretical Neurosciencde course lecture slides (also with some other useful resources)
- some introductory material and a collection of notes written by Gatsby students can be found here
- David Heeger’s lecture handouts
- ModelDB is a database for computational neuroscience models with published code
- CRCNS for publicly available neuroscience data sets
- NeuroRumblr for neuro job listings, career advice and conferences
- Loren Frank’s notes on giving good talks and mentoring advice

#### Books

- Dayan & Abbott’s Theoretical Neuroscience textbook
- Gerstner’s Neuronal Dynamics textbook
- Principles of Computational Modelling in Neuroscience

**Important Stuff**

- Collection of resources on diversity in academia
- Bias watch Neuro
- Anne’s List
- Strike For Black Lives – list of resources
- Concrete Steps for Recruiting, Supporting, and Advancing Underrepresented Minoritized Scientists