Difference between revisions of "Learning Resources"

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== Bayesian Nonparametrics ==
 
== Bayesian Nonparametrics ==
 
* [http://stat.columbia.edu/~porbanz/npb-tutorial.html Peter Orbanz' website]
 
* [http://stat.columbia.edu/~porbanz/npb-tutorial.html Peter Orbanz' website]
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* [http://www.shadmehrlab.org/lectures.html Reza Shadmehr lectures on motor control & learning]
 
* [http://www.shadmehrlab.org/lectures.html Reza Shadmehr lectures on motor control & learning]
 
* [http://neural-reckoning.org/comp-neuro-resources.html Dan Goodman's compilation of Comp Neuro learning materials]
 
* [http://neural-reckoning.org/comp-neuro-resources.html Dan Goodman's compilation of Comp Neuro learning materials]
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* [https://www.cns.nyu.edu/~eero/math-tools/ Mathematical Tools for Neural and Cognitive Science] - from Mike Landy and Eero Simoncelli
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* [https://www.simonsfoundation.org/collaborations/global-brain/online-resources-for-systems-and-computational-neuroscience Simon's Foundation Online resources] for systems and computational neuroscience
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* [https://github.com/NeuromatchAcademy/precourse/blob/master/resources.md NMA list of resources]
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* [https://neuronaldynamics.epfl.ch/index.html Gerstner's Neuronal Dynamics book] - free online version with Python exercises using Brian 2
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* [https://computationalcognitivescience.github.io/lovelace/ Theoretical modeling for cognitive science and psychology] (free) - online book by Mark Blokpoel and Iris van Rooij
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* [https://algorithmsbook.com/ Algorithms for decision making] by Kochenderfer, Wheeler, and Wray (free PDF)
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* [https://direct.mit.edu/books/book/3159/Computational-Modeling-Methods-for-Neuroscientists Computational Modeling Methods for Neuroscientists] - (free PDF) by Erik De Schutter
  
 
== Information Theory ==
 
== Information Theory ==
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== Machine Learning ==
 
== Machine Learning ==
 
==== Books ====
 
==== Books ====
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* [http://databookuw.com/ Data-driven Science and Engineering: Machine Learning, Dynamical Systems, and Control]
 
* [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]
 
* [http://ciml.info/dl/v0_8/ciml-v0_8-all.pdf Daume's book]
 
* [http://ciml.info/dl/v0_8/ciml-v0_8-all.pdf Daume's book]
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* [https://www.cs.ubc.ca/~murphyk/MLbook/pml-intro-22may12.pdf Murphy's book]
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* [https://probml.github.io/pml-book/ Murphy's book]
 
* [http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf Shalev-Shwartz & Ben-David's book]
 
* [http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf Shalev-Shwartz & Ben-David's book]
 
* [http://ai.stanford.edu/~nilsson/MLBOOK.pdf Nilsson's book]
 
* [http://ai.stanford.edu/~nilsson/MLBOOK.pdf Nilsson'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).
 
* [http://jim-stone.staff.shef.ac.uk/BookBayes2012/books_by_jv_stone/index.html Books] by J V Stone.
 
* [http://jim-stone.staff.shef.ac.uk/BookBayes2012/books_by_jv_stone/index.html Books] by J V Stone.
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* '''[http://d2l.ai/ Zhang et al. book with Python tutorials!]'''
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* [http://users.isr.ist.utl.pt/~wurmd/Livros/school/Bishop%20-%20Pattern%20Recognition%20And%20Machine%20Learning%20-%20Springer%20%202006.pdf Bishop's Pattern Recognition and Machine Learning book]
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* [https://mlstory.org/ PATTERNS, PREDICTIONS, AND ACTIONS: A story about machine learning] - amazing free online book by Hardt & Recht
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* [https://deeplearningtheory.com/ The Principles of Deep Learning Theory] - free online version by Roberts & Yaida
  
 
==== Online Resources ====
 
==== Online Resources ====
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*[http://download.springer.com/static/pdf/772/bok%253A978-1-4614-4984-3.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Fbook%2F10.1007%2F978-1-4614-4984-3&token2=exp=1474042019~acl=%2Fstatic%2Fpdf%2F772%2Fbok%25253A978-1-4614-4984-3.pdf%3ForiginUrl%3Dhttp%253A%252F%252Flink.springer.com%252Fbook%252F10.1007%252F978-1-4614-4984-3*~hmac=0a460b39cb2453dbeb442d3ac78432ef059788bc44ff8e4fe1c62fb8f57ec95f Imaging Brain Function with EEG: Advanced Temporal and Spatial Analysis of EEG Signals (Book by Walter J. Freeman)]
 
*[http://download.springer.com/static/pdf/772/bok%253A978-1-4614-4984-3.pdf?originUrl=http%3A%2F%2Flink.springer.com%2Fbook%2F10.1007%2F978-1-4614-4984-3&token2=exp=1474042019~acl=%2Fstatic%2Fpdf%2F772%2Fbok%25253A978-1-4614-4984-3.pdf%3ForiginUrl%3Dhttp%253A%252F%252Flink.springer.com%252Fbook%252F10.1007%252F978-1-4614-4984-3*~hmac=0a460b39cb2453dbeb442d3ac78432ef059788bc44ff8e4fe1c62fb8f57ec95f Imaging Brain Function with EEG: Advanced Temporal and Spatial Analysis of EEG Signals (Book by Walter J. Freeman)]
 
* [https://webfiles.uci.edu/mdlee/LeeWagenmakers2013_Free.pdf Bayesian Cognitive Modeling: A Practical Course]
 
* [https://webfiles.uci.edu/mdlee/LeeWagenmakers2013_Free.pdf Bayesian Cognitive Modeling: A Practical Course]
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* [https://github.com/ebatty/MathToolsforNeuroscience Math tools for Neuroscience] - very cool intro to basic Math by NMA's Ella Batty et al.
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* [https://john-s-butler-dit.github.io/NumericalAnalysisBook/?s=03 Numerical Analysis with Applications in Python] - (free JupyterBook) by John Butler
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* [https://www.biodyn.ro/course/literatura/Nonlinear_Dynamics_and_Chaos_2018_Steven_H._Strogatz.pdf Nonlinear Dynamics And Chaos] book by Steven H. Strogatz
  
 
== MATLAB ==
 
== MATLAB ==
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* [[Media:Curve_Fitting.pdf | Jerry Jeyachandra's Curve Fitting Tutorial]]
 
* [[Media:Curve_Fitting.pdf | Jerry Jeyachandra's Curve Fitting Tutorial]]
 
**[[Media:CurveFit_Tutorial.zip | Curve Fitting Scripts]]
 
**[[Media:CurveFit_Tutorial.zip | Curve Fitting Scripts]]
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* [https://www.youtube.com/c/Eigensteve Steve Brunton's amazing Youtube videos] explaining many different Math concepts
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* [https://www.matlabcoding.com/2023/12/matlab-for-neuroscientists-introduction.html Matlab for Neuroscientists] (free PDF) 2nd edition, by Pascal Wallish et al.
  
 
== Python ==
 
== Python ==
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* [https://www.coursera.org/learn/interactive-python-1 Intro to interactive programming] in Python
 
* [https://www.coursera.org/learn/interactive-python-1 Intro to interactive programming] in Python
 
* [https://xcorr.net/2020/02/21/transitioning-away-from-matlab/ Making the transition from Matlab to Python]
 
* [https://xcorr.net/2020/02/21/transitioning-away-from-matlab/ Making the transition from Matlab to Python]
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* [https://medium.com/@thomas.a.dorfer/artefact-correction-with-ica-53afb63ad300 ICA-based EEG artifact removal in Python]
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* [https://carpentries.org/blog/2021/07/pyrse-book/?s=03 The Carpentries - Research Software Engineering with Python (book)]
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* [https://goodresearch.dev/ The Good Research Code Handbook] - an amazing resource by Patrick Minault
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* [https://www.ethanrosenthal.com/2022/02/01/everything-gets-a-package/ Setting up a data science project] - practical advice including package management by Ethan Rosenthal
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* [https://virati.medium.com/make-your-code-last-forever-18e5bd3e4842 How to use containers for code] - by Vineet Tiruvadi
  
 
== Statistics ==
 
== Statistics ==
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* [https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/ Causal Inference book]
 
* [https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/ Causal Inference book]
 
* [https://elifesciences.org/articles/48175 Ten common statistical mistakes to watch out for when writing or reviewing a manuscript]
 
* [https://elifesciences.org/articles/48175 Ten common statistical mistakes to watch out for when writing or reviewing a manuscript]
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* [https://statsthinking21.github.io/statsthinking21-core-site/ "Statistical Thinking for the 21st Century"] free online book by Russell A. Poldrack
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* [http://www.stat.columbia.edu/~gelman/book/ "Bayesian Data Analysis"] book by Andrew Gelman et al. with examples in Python and R
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* [https://www.nature.com/articles/s41593-020-0660-4 Using Bayes factor to compute evidence of absence / absence of evidence]
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* [https://probml.github.io/pml-book/book1.html KP Murphy's book: Probabilistic Machine Learning: An Introduction] - free
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* [http://www.inference.org.uk/mackay/itila/ D MacKay's Information Theory, Inference, and Learning Algorithms book] - free
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* [http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.Online D Barber's Bayesian Reasoning and Machine Learning book] - free
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* [https://probability4datascience.com/?s=03 Introduction to Probability for Data Science] by Stanley Chan - free online book with Python exercises!
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* [https://lakens.github.io/statistical_inferences/index.html?s=03 Improving your statistical inferences] by Daniel Lakens - free online book with R code
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* [https://bruno.nicenboim.me/bayescogsci/ An Introduction to Bayesian Data Analysis for Cognitive Science] free online book by Bruno Nicenboim, Daniel J. Schad, and Shravan Vasishth
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== Neuroimaging analyses ==
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* [http://mikexcohen.com/lectures.html?s=03 Mike Cohen's EEG analysis course]
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* [http://neuroimaging-data-science.org/root.html Neuroimaging and Data Science book] - (free) by Ariel Rokem and Tal Yarkoni

Latest revision as of 19:45, 22 October 2024

Bayesian Nonparametrics

General Computational Neuroscience

Information Theory

Machine Learning

Books

Online Resources

Journal Club Tutorials

See Journal Club#Tutorials.

General Math

MATLAB

Python

Statistics

Neuroimaging analyses

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