Learning Resources
Contents
Bayesian Nonparametrics
- Peter Orbanz' website
- An example of Dirichlet processes applied to computational cognitive science (language learning from statistical regularities in speech).
- An example of Dirichlet processes applied to individual differences.
- Any of the papers of Radford Neal.
- The PhD theses of Shane Jensen, Erik Sudderth, and Derek Blythe.
General Computational Neuroscience
- ELSC Youtube Channel Contains archived computational neuroscience seminars and lectures, including Dayan, Abbot, Pouget..., as well as physiology resources.
- Coursera Computational Neuroscience With instructors Rajesh Rao and Adrienne Fairhall.
- Reza Shadmehr lectures on motor control & learning
- Dan Goodman's compilation of Comp Neuro learning materials
- Mathematical Tools for Neural and Cognitive Science - from Mike Landy and Eero Simoncelli
- Simon's Foundation Online resources for systems and computational neuroscience
- NMA list of resources
- Gerstner's Neuronal Dynamics book - free online version with Python exercises using Brian 2
Information Theory
- A Tutorial for Information Theory in Neuroscience
- Information Theory, Inference, and Learning Algorithms - An excellent book that presents Bayesian inference and machine learning from the perspective of coding/information theory.
- Estimation of mutual information for continuous random variables.
Machine Learning
Books
- Tom Mitchell's book
- Smola & Vishwanathan's book
- Daume's book
- Murphy's book
- Shalev-Shwartz & Ben-David's book
- Nilsson's book
- Harrington's book
- Ian Goodfellow, Yoshua Bengio, and Aaron Courville's book
- Hastie, Tibshirani, and Friedman's book; also James, Witten, Hastie, and Tibshirani's book, which has less focus on mathematical foundations and more on applications (in R).
- Books by J V Stone.
- Zhang et al. book with Python tutorials!
Online Resources
- ML course by Stanford computer scientist Andrew Ng (requires Coursera signup --> free enrollment)
- Andrew Ng ML course exercises for Python
- Markov Chains explained visually
- Mo Chen's toolbox for all the methods discussed in the book: Pattern Recognition and Machine Learning by C. Bishop
- arXiv Sanity Preserver, an interface to the machine learning section of arXiv; lists recent papers most discussed in social media, and gives similar paper recommendations.
- Microsoft Professional Program for Artificial Intelligence (free to audit)
Journal Club Tutorials
General Math
- Matrix cookbook
- Some useful vector/matrix identities.
- Image filtering, edge detection, etc. (Computer vision)
- Hough transform tutorial (Computer vision)
- Introductory tutorial on Wavelet transforms
- Imaging Brain Function with EEG: Advanced Temporal and Spatial Analysis of EEG Signals (Book by Walter J. Freeman)
- Bayesian Cognitive Modeling: A Practical Course
- Math tools for Neuroscience - very cool intro to basic Math by NMA's Ella Batty et al.
MATLAB
- Intro to programming with Matlab
- Moler's tutorials
- MatLab Style Guidelines ("The goal of these guidelines is to help produce code that is more likely to be correct, understandable, sharable and maintainable.")
- Scott Murdison's compilation
- Jerry Jeyachandra's Curve Fitting Tutorial
- Steve Brunton's amazing Youtube videos explaining many different Math concepts
Python
- Codecademy tutorial to learn Python from scratch
- Intro to interactive programming in Python
- Making the transition from Matlab to Python
- ICA-based EEG artifact removal in Python
Statistics
- Rice University online Stats book
- Markov Chain Monte Carlo in practice - book
- Bootstrap methods & significance estimation
- how to do repeated measures ANOVA in Matlab (by Parisa)
- Ten Simple Rules for Effective Statistical Practice
- Testing Hypotheses About Psychometric Functions
- Latin square method for experimental design
- Causal Inference book
- Ten common statistical mistakes to watch out for when writing or reviewing a manuscript
- "Statistical Thinking for the 21st Century" free online book by Russell A. Poldrack
- "Bayesian Data Analysis" book by Andrew Gelman et al. with examples in Python and R
- Using Bayes factor to compute evidence of absence / absence of evidence
- KP Murphy's book: Probabilistic Machine Learning: An Introduction - free
- D MacKay's Information Theory, Inference, and Learning Algorithms book - free
- D Barber's Bayesian Reasoning and Machine Learning book - free
