Blohm Lab Wiki

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
• Theoretical modeling for cognitive science and psychology (free) - online book by Mark Blokpoel and Iris van Rooij
• Algorithms for decision making by Kochenderfer, Wheeler, and Wray (free PDF)
• Computational Modeling Methods for Neuroscientists - (free PDF) by Erik De Schutter

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

• Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control
• 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!
• Bishop's Pattern Recognition and Machine Learning book
• PATTERNS, PREDICTIONS, AND ACTIONS: A story about machine learning - amazing free online book by Hardt & Recht
• The Principles of Deep Learning Theory - free online version by Roberts & Yaida

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

See 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.
• Numerical Analysis with Applications in Python - (free JupyterBook) by John Butler

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
  • Curve Fitting Scripts
• 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
• The Carpentries - Research Software Engineering with Python (book)
• The Good Research Code Handbook - an amazing resource by Patrick Minault
• Setting up a data science project - practical advice including package management by Ethan Rosenthal
• How to use containers for code - by Vineet Tiruvadi

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
• Introduction to Probability for Data Science by Stanley Chan - free online book with Python exercises!
• Improving your statistical inferences by Daniel Lakens - free online book with R code

Neuroimaging analyses

• Mike Cohen's EEG analysis course
• Neuroimaging and Data Science book - (free) by Ariel Rokem and Tal Yarkoni