Difference between revisions of "Learning Resources"
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| + | == Bayesian Nonparametrics == | ||
| + | * [http://stat.columbia.edu/~porbanz/npb-tutorial.html Peter Orbanz' website] | ||
| + | * [https://ac.els-cdn.com/S0010027709000675/1-s2.0-S0010027709000675-main.pdf?_tid=657290fd-bbe2-4091-8421-08fb0ddb4bf8&acdnat=1544463842_6ee3b26397aade4fd4dd19560e1fbcd0 An example] of Dirichlet processes applied to computational cognitive science (language learning from statistical regularities in speech). | ||
| + | * [https://cocosci.berkeley.edu/tom/papers/indivdiffs_jmp.pdf An example] of Dirichlet processes applied to individual differences. | ||
| + | * [https://scholar.google.ca/citations?user=rr8pZoUAAAAJ&hl=en&oi=ao Any of the papers] of Radford Neal. | ||
| + | * The PhD theses of [http://www-stat.wharton.upenn.edu/~stjensen/papers/shanejensen.phdthesis04.pdf Shane Jensen], [http://cs.brown.edu/~sudderth/papers/sudderthPhD.pdf Erik Sudderth], and [https://lib.dr.iastate.edu/etd/13787/ Derek Blythe]. | ||
== General Computational Neuroscience == | == General Computational Neuroscience == | ||
Revision as of 20:38, 11 December 2018
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
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.
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.
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
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
Python
- Codecademy tutorial to learn Python from scratch
- Intro to interactive programming 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
