Difference between revisions of "Learning Resources"
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* [http://www.leg.ufpr.br/~eder/Markov/Markov%20Chain%20Monte%20Carlo%20In%20Practice%20.pdf Markov Chain Monte Carlo in practice] - book | * [http://www.leg.ufpr.br/~eder/Markov/Markov%20Chain%20Monte%20Carlo%20In%20Practice%20.pdf Markov Chain Monte Carlo in practice] - book | ||
* [http://statweb.stanford.edu/~tibs/stat315a/Supplements/bootstrap.pdf Bootstrap methods & significance estimation] | * [http://statweb.stanford.edu/~tibs/stat315a/Supplements/bootstrap.pdf Bootstrap methods & significance estimation] | ||
| − | * how to do [[Media: | + | * how to do [[Media:Repeated_ANOVA_MATLAB_v2.pdf | repeated measures ANOVA]] in Matlab (by Parisa) |
* [http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004961 Ten Simple Rules for Effective Statistical Practice] | * [http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004961 Ten Simple Rules for Effective Statistical Practice] | ||
* [http://bootstrap-software.com/psignifit/publications/hill2001.pdf Testing Hypotheses About Psychometric Functions] | * [http://bootstrap-software.com/psignifit/publications/hill2001.pdf Testing Hypotheses About Psychometric Functions] | ||
* [[Media:Latin_square_Method.pdf | Latin square method for experimental design]] | * [[Media:Latin_square_Method.pdf | Latin square method for experimental design]] | ||
Revision as of 17:08, 18 October 2018
Contents
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
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
- 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
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
