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] | ||
| + | * [https://www.cns.nyu.edu/~eero/math-tools/ Mathematical Tools for Neural and Cognitive Science] - from Mike Landy and Eero Simoncelli | ||
| + | * [https://www.simonsfoundation.org/collaborations/global-brain/online-resources-for-systems-and-computational-neuroscience Simon's Foundation Online resources] for systems and computational neuroscience | ||
| + | * [https://github.com/NeuromatchAcademy/precourse/blob/master/resources.md NMA list of resources] | ||
| + | * [https://neuronaldynamics.epfl.ch/index.html Gerstner's Neuronal Dynamics book] - free online version with Python exercises using Brian 2 | ||
| + | * [https://computationalcognitivescience.github.io/lovelace/ Theoretical modeling for cognitive science and psychology] (free) - online book by Mark Blokpoel and Iris van Rooij | ||
| + | * [https://algorithmsbook.com/ Algorithms for decision making] by Kochenderfer, Wheeler, and Wray (free PDF) | ||
| + | * [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 ==== | ||
| + | * [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] | ||
| − | * [https:// | + | * [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. | ||
| + | * '''[http://d2l.ai/ Zhang et al. book with Python tutorials!]''' | ||
| + | * [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] | ||
| + | * [https://mlstory.org/ PATTERNS, PREDICTIONS, AND ACTIONS: A story about machine learning] - amazing free online book by Hardt & Recht | ||
| + | * [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] | ||
| + | * [https://github.com/ebatty/MathToolsforNeuroscience Math tools for Neuroscience] - very cool intro to basic Math by NMA's Ella Batty et al. | ||
| + | * [https://john-s-butler-dit.github.io/NumericalAnalysisBook/?s=03 Numerical Analysis with Applications in Python] - (free JupyterBook) by John Butler | ||
| + | * [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]] | ||
| + | * [https://www.youtube.com/c/Eigensteve Steve Brunton's amazing Youtube videos] explaining many different Math concepts | ||
| + | * [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 == | ||
* [https://www.codecademy.com/learn/learn-python Codecademy tutorial] to learn Python from scratch | * [https://www.codecademy.com/learn/learn-python Codecademy tutorial] to learn Python from scratch | ||
* [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://medium.com/@thomas.a.dorfer/artefact-correction-with-ica-53afb63ad300 ICA-based EEG artifact removal in Python] | ||
| + | * [https://carpentries.org/blog/2021/07/pyrse-book/?s=03 The Carpentries - Research Software Engineering with Python (book)] | ||
| + | * [https://goodresearch.dev/ The Good Research Code Handbook] - an amazing resource by Patrick Minault | ||
| + | * [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 | ||
| + | * [https://virati.medium.com/make-your-code-last-forever-18e5bd3e4842 How to use containers for code] - by Vineet Tiruvadi | ||
== Statistics == | == Statistics == | ||
| Line 73: | Line 95: | ||
* [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] | ||
| + | * [https://statsthinking21.github.io/statsthinking21-core-site/ "Statistical Thinking for the 21st Century"] free online book by Russell A. Poldrack | ||
| + | * [http://www.stat.columbia.edu/~gelman/book/ "Bayesian Data Analysis"] book by Andrew Gelman et al. with examples in Python and R | ||
| + | * [https://www.nature.com/articles/s41593-020-0660-4 Using Bayes factor to compute evidence of absence / absence of evidence] | ||
| + | * [https://probml.github.io/pml-book/book1.html KP Murphy's book: Probabilistic Machine Learning: An Introduction] - free | ||
| + | * [http://www.inference.org.uk/mackay/itila/ D MacKay's Information Theory, Inference, and Learning Algorithms book] - free | ||
| + | * [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 | ||
| + | * [https://probability4datascience.com/?s=03 Introduction to Probability for Data Science] by Stanley Chan - free online book with Python exercises! | ||
| + | * [https://lakens.github.io/statistical_inferences/index.html?s=03 Improving your statistical inferences] by Daniel Lakens - free online book with R code | ||
| + | * [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 | ||
| + | |||
| + | == Neuroimaging analyses == | ||
| + | * [http://mikexcohen.com/lectures.html?s=03 Mike Cohen's EEG analysis course] | ||
| + | * [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
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
- 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
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
- Nonlinear Dynamics And Chaos book by Steven H. Strogatz
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
- Steve Brunton's amazing Youtube videos explaining many different Math concepts
- Matlab for Neuroscientists (free PDF) 2nd edition, by Pascal Wallish et al.
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
- An Introduction to Bayesian Data Analysis for Cognitive Science free online book by Bruno Nicenboim, Daniel J. Schad, and Shravan Vasishth
Neuroimaging analyses
- Mike Cohen's EEG analysis course
- Neuroimaging and Data Science book - (free) by Ariel Rokem and Tal Yarkoni
