# Other Resources
## Courses
1. [NYU CDS: Probability and Statistics](https://cims.nyu.edu/~cfgranda/pages/DSGA1002_fall17/index.html)
1. [Stanford Probability and Statistics](http://cs229.stanford.edu/section/cs229-prob.pdf)
1. [NYU CDS: Inference and Representation](https://inf16nyu.github.io/home/)
1. [NYU CDS: Big Data 2015](https://www.vistrails.org/index.php/Course:_Big_Data_2015)
1. [NYU CDS: Machine Learning](https://davidrosenberg.github.io/ml2017/#resources)
1. [Foundations of Graphical Models by David Blei](http://www.cs.columbia.edu/~blei/fogm/2016F/) -- see [Basics of Graphical Models](http://www.cs.columbia.edu/~blei/fogm/2016F/doc/graphical-models.pdf)
1. see also [a video on d-separation by Pieter Abbeel](https://www.youtube.com/watch?v=yDs_q6jKHb0)
1. semantics of graphical models (here called "Boiler plate diagrams") and an extended visual language [Directed Factor Graph Notation for Generative Models
Laura Dietz](https://github.com/jluttine/tikz-bayesnet/blob/master/dietz-techreport.pdf), which is the basis of the `tikz-bayesnet` package
1. [Algorithms for Convex Optimization by Nisheeth K. Vishnoi](https://convex-optimization.github.io)
1. [Introduction to Causal Inference by Brady Neal](https://www.bradyneal.com/causal-inference-course)
1. [Michael Jordan's lecture notes on notes on Probabilistic Graphical Models](https://people.eecs.berkeley.edu/%7Ejordan/prelims/)
1. [MIT lecture notes on algorithms for inference](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-438-algorithms-for-inference-fall-2014/lecture-notes/)
1. [Kevin Murphy, Machine Learning: a Probabilistic Perspective (4th eddition)](http://www.cs.ubc.ca/%7Emurphyk/MLbook/index.html) | [online @ NYU Libraries](http://site.ebrary.com/lib/nyulibrary/detail.action?docID=10597102).
1. [Probabilistic Programming and Bayesian Methods for Hackers by Cam Davidson Pilon](https://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/)
## Short courses / tutorials
1. [Basic Python](https://swcarpentry.github.io/python-novice-inflammation/)
1. [Plotting and Programming with Python](https://swcarpentry.github.io/python-novice-gapminder/)
## Linear Algebra
1. [Essence of linear algebra youtube videos by 3blue1brown](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab)
1. [Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares, Stephen Boyd and Lieven Vandenberghe](http://vmls-book.stanford.edu)
1. [Linear dynamical systems](https://www.youtube.com/watch?v=bf1264iFr-w&list=PLzvEnvQ9sS15pwCo8DYnJ-gArIkKZwJjF)
1. [Linear Algebra done right](https://linear.axler.net)
1. [NUMERICAL LINEAR ALGEBRA Lloyd N. Trefethen and David Bau, III](https://people.maths.ox.ac.uk/trefethen/text.html)
1. [Scientific Computing for PhDs](http://podcasts.ox.ac.uk/series/scientific-computing-dphil-students)
## Books
1. [All of Statistics by Wasserman](https://www.amazon.com/All-Statistics-Statistical-Inference-Springer/dp/1441923225)
1. [PRML](https://github.com/cranmer/PRML)
1. [Mathematics for Machine Learning](https://mml-book.github.io)
1. [Elements of Causal Inference by Jonas Peters, Dominik Janzing and Bernhard Schölkopf](https://mitpress.mit.edu/books/elements-causal-inference) [free PDF](https://www.dropbox.com/s/dl/gkmsow492w3oolt/11283.pdf)
1. [Trevor Hastie, Rob Tibshirani, and Jerry Friedman, Elements of Statistical Learning, Second Edition, Springer, 2009](https://web.stanford.edu/~hastie/ElemStatLearn//)
## Influential texts
1. [Knuth Calculus](https://micromath.wordpress.com/2008/04/14/donald-knuth-calculus-via-o-notation/)
1. [Functional Differential Geometry by Gerald Jay Sussman and Jack Wisdom](https://mitpress.mit.edu/books/functional-differential-geometry)
## Misc
1. [NeurIPS astro tutorial with datasets etc.](https://dwh.gg/NeurIPSastro)
1. [Paper about statistical combinations from phys/astro authors](https://arxiv.org/abs/2012.09874)
1. [Gentle Introduction to Automatic Differentiation on Kaggle](https://www.kaggle.com/borisettinger/gentle-introduction-to-automatic-differentiation)
1. [Short notes on divergence measures by Danilo Rezende](https://danilorezende.com/wp-content/uploads/2018/07/divergences.pdf)
1. [Lecture notes on: Information-theoretic methods for high-dimensional statistics, by Yihong Wu](http://www.stat.yale.edu/~yw562/teaching/it-stats.pdf)
## Meta