Blohm Lab Wiki

== 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 ==
* [https://www.youtube.com/user/elscvideo/videos ELSC Youtube Channel] Contains archived computational neuroscience seminars and lectures, including Dayan, Abbot, Pouget..., as well as physiology resources.
* [https://www.coursera.org/learn/computational-neuroscience Coursera Computational Neuroscience] With instructors Rajesh Rao and Adrienne Fairhall.
* [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]
* [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 ==
* [http://www.eneuro.org/content/5/3/ENEURO.0052-18.2018?cpetoc A Tutorial for Information Theory in Neuroscience] 
* [http://www.inference.org.uk/mackay/itila/book.html Information Theory, Inference, and Learning Algorithms] - An excellent book that presents Bayesian inference and machine learning from the perspective of coding/information theory.
* [https://journals.aps.org/pre/pdf/10.1103/PhysRevE.69.066138 Estimation of mutual information for continuous random variables.]

== Machine Learning ==
==== 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://alex.smola.org/drafts/thebook.pdf Smola & Vishwanathan's book]
* [http://ciml.info/dl/v0_8/ciml-v0_8-all.pdf Daume's book]
* [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://ai.stanford.edu/~nilsson/MLBOOK.pdf Nilsson's book]
* [http://www2.ift.ulaval.ca/~chaib/IFT-4102-7025/public_html/Fichiers/Machine_Learning_in_Action.pdf Harrington's book]
* [http://www.deeplearningbook.org/ Ian Goodfellow, Yoshua Bengio, and Aaron Courville's book]
* [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://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 ====
* [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://setosa.io/ev/markov-chains/ Markov Chains explained visually]
* [https://www.mathworks.com/matlabcentral/fileexchange/55826-pattern-recognition-and-machine-learning-toolbox Mo Chen's toolbox] for all the methods discussed in the book: Pattern Recognition and Machine Learning by C. Bishop
* [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.
* Microsoft [https://academy.microsoft.com/en-us/professional-program/tracks/artificial-intelligence/ Professional Program] for Artificial Intelligence (free to audit)

==== Journal Club Tutorials ====
See [[Journal Club#Tutorials]].

== General Math ==
* [https://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf Matrix cookbook]
* [https://tminka.github.io/papers/matrix/minka-matrix.pdf Some useful vector/matrix identities]. 
* [http://www.cs.toronto.edu/~urtasun/courses/CV/lecture02.pdf Image filtering, edge detection, etc. (Computer vision)]
* [http://homepages.inf.ed.ac.uk/rbf/HIPR2/hough.htm Hough transform tutorial (Computer vision)]
* [http://www.cse.unr.edu/~bebis/CS474/Handouts/WaveletTutorial.pdf Introductory tutorial on Wavelet transforms]
*[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://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 ==
* [https://www.coursera.org/learn/matlab Intro to programming] with Matlab
* [http://www.mathworks.com/moler/exm/chapters.html?refresh=true Moler's tutorials]
* [https://www.ee.columbia.edu/~marios/matlab/MatlabStyle1p5.pdf MatLab Style Guidelines] ("The goal of these guidelines is to help produce code that is more likely to be correct, understandable, sharable and maintainable.")
* [[Media:Matlab_intro.pdf | Scott Murdison's compilation]]
* [[Media:Curve_Fitting.pdf | Jerry Jeyachandra's Curve Fitting Tutorial]]
**[[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 ==
* [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://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 ==
* [http://onlinestatbook.com/2/index.html Rice University online Stats 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]
* 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://bootstrap-software.com/psignifit/publications/hill2001.pdf Testing Hypotheses About Psychometric Functions]
* [[Media:Latin_square_Method.pdf | Latin square method for experimental design]]
* [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://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

== 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