Learning Resources: Difference between revisions
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== Machine Learning == | == Machine Learning == | ||
=== Books === | ==== Books ==== | ||
* [http://personal.disco.unimib.it/Vanneschi/McGrawHill_-_Machine_Learning_-Tom_Mitchell.pdf Tom Mitchell's book] | * [http://personal.disco.unimib.it/Vanneschi/McGrawHill_-_Machine_Learning_-Tom_Mitchell.pdf Tom Mitchell's book] | ||
* [http://alex.smola.org/drafts/thebook.pdf Smola & Vishwanathan's book] | * [http://alex.smola.org/drafts/thebook.pdf Smola & Vishwanathan's book] | ||
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* [https://web.stanford.edu/~hastie/Papers/ESLII.pdf Hastie, Tibshirani, and Friedman's book]; also [http://www-bcf.usc.edu/~gareth/ISL/ James, Witten, Hastie, and Tibshirani's book], which has less focus on mathematical foundations and more on applications (in R). | * [https://web.stanford.edu/~hastie/Papers/ESLII.pdf Hastie, Tibshirani, and Friedman's book]; also [http://www-bcf.usc.edu/~gareth/ISL/ James, Witten, Hastie, and Tibshirani's book], which has less focus on mathematical foundations and more on applications (in R). | ||
=== Online Resources == | ==== Online Resources ==== | ||
* [https://www.coursera.org/learn/machine-learning ML course by Stanford computer scientist Andrew Ng (requires Coursera signup --> free enrollment)] | * [https://www.coursera.org/learn/machine-learning ML course by Stanford computer scientist Andrew Ng (requires Coursera signup --> free enrollment)] | ||
* [http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-1/ Andrew Ng ML course exercises for Python] | * [http://www.johnwittenauer.net/machine-learning-exercises-in-python-part-1/ Andrew Ng ML course exercises for Python] | ||
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* [http://www.arxiv-sanity.com 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. | * [http://www.arxiv-sanity.com 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. | ||
=== Journal Club Tutorials === | ==== Journal Club Tutorials ==== | ||
See [[Journal Club#Tutorials]]. | See [[Journal Club#Tutorials]]. | ||
Revision as of 19:10, 4 April 2018
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).
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.
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
