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<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#courses">
Courses
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<li class="toc-h2 nav-item toc-entry">
<a class="reference internal nav-link" href="#short-courses-tutorials">
Short courses / tutorials
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Linear Algebra
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Books
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Influential texts
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Misc
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<div>
<div class="section" id="other-resources">
<h1>Other Resources<a class="headerlink" href="#other-resources" title="Permalink to this headline">¶</a></h1>
<div class="section" id="courses">
<h2>Courses<a class="headerlink" href="#courses" title="Permalink to this headline">¶</a></h2>
<ol class="simple">
<li><p><a class="reference external" href="https://cims.nyu.edu/~cfgranda/pages/DSGA1002_fall17/index.html">NYU CDS: Probability and Statistics</a></p></li>
<li><p><a class="reference external" href="http://cs229.stanford.edu/section/cs229-prob.pdf">Stanford Probability and Statistics</a></p></li>
<li><p><a class="reference external" href="https://inf16nyu.github.io/home/">NYU CDS: Inference and Representation</a></p></li>
<li><p><a class="reference external" href="https://www.vistrails.org/index.php/Course:_Big_Data_2015">NYU CDS: Big Data 2015</a></p></li>
<li><p><a class="reference external" href="https://davidrosenberg.github.io/ml2017/#resources">NYU CDS: Machine Learning</a></p></li>
<li><p><a class="reference external" href="http://www.cs.columbia.edu/~blei/fogm/2016F/">Foundations of Graphical Models by David Blei</a> – see <a class="reference external" href="http://www.cs.columbia.edu/~blei/fogm/2016F/doc/graphical-models.pdf">Basics of Graphical Models</a></p>
<ol class="simple">
<li><p>see also <a class="reference external" href="https://www.youtube.com/watch?v=yDs_q6jKHb0">a video on d-separation by Pieter Abbeel</a></p></li>
<li><p>semantics of graphical models (here called “Boiler plate diagrams”) and an extended visual language <a class="reference external" href="https://github.com/jluttine/tikz-bayesnet/blob/master/dietz-techreport.pdf">Directed Factor Graph Notation for Generative Models
Laura Dietz</a>, which is the basis of the <code class="docutils literal notranslate"><span class="pre">tikz-bayesnet</span></code> package</p></li>
</ol>
</li>
<li><p><a class="reference external" href="https://convex-optimization.github.io">Algorithms for Convex Optimization by Nisheeth K. Vishnoi</a></p></li>
<li><p><a class="reference external" href="https://www.bradyneal.com/causal-inference-course">Introduction to Causal Inference by Brady Neal</a></p></li>
<li><p><a class="reference external" href="https://people.eecs.berkeley.edu/%7Ejordan/prelims/">Michael Jordan’s lecture notes on notes on Probabilistic Graphical Models</a></p></li>
<li><p><a class="reference external" href="http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-438-algorithms-for-inference-fall-2014/lecture-notes/">MIT lecture notes on algorithms for inference</a></p></li>
<li><p><a class="reference external" href="http://www.cs.ubc.ca/%7Emurphyk/MLbook/index.html">Kevin Murphy, Machine Learning: a Probabilistic Perspective (4th eddition)</a> | <a class="reference external" href="http://site.ebrary.com/lib/nyulibrary/detail.action?docID=10597102">online @ NYU Libraries</a>.</p></li>
<li><p><a class="reference external" href="https://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/">Probabilistic Programming and Bayesian Methods for Hackers by Cam Davidson Pilon</a></p></li>
</ol>
</div>
<div class="section" id="short-courses-tutorials">
<h2>Short courses / tutorials<a class="headerlink" href="#short-courses-tutorials" title="Permalink to this headline">¶</a></h2>
<ol class="simple">
<li><p><a class="reference external" href="https://swcarpentry.github.io/python-novice-inflammation/">Basic Python</a></p></li>
<li><p><a class="reference external" href="https://swcarpentry.github.io/python-novice-gapminder/">Plotting and Programming with Python</a></p></li>
</ol>
</div>
<div class="section" id="linear-algebra">
<h2>Linear Algebra<a class="headerlink" href="#linear-algebra" title="Permalink to this headline">¶</a></h2>
<ol class="simple">
<li><p><a class="reference external" href="https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab">Essence of linear algebra youtube videos by 3blue1brown</a></p></li>
<li><p><a class="reference external" href="http://vmls-book.stanford.edu">Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares, Stephen Boyd and Lieven Vandenberghe</a></p></li>
<li><p><a class="reference external" href="https://www.youtube.com/watch?v=bf1264iFr-w&list=PLzvEnvQ9sS15pwCo8DYnJ-gArIkKZwJjF">Linear dynamical systems</a></p></li>
<li><p><a class="reference external" href="https://linear.axler.net">Linear Algebra done right</a></p></li>
<li><p><a class="reference external" href="https://people.maths.ox.ac.uk/trefethen/text.html">NUMERICAL LINEAR ALGEBRA Lloyd N. Trefethen and David Bau, III</a></p></li>
<li><p><a class="reference external" href="http://podcasts.ox.ac.uk/series/scientific-computing-dphil-students">Scientific Computing for PhDs</a></p></li>
</ol>
</div>
<div class="section" id="books">
<h2>Books<a class="headerlink" href="#books" title="Permalink to this headline">¶</a></h2>
<ol class="simple">
<li><p><a class="reference external" href="https://www.amazon.com/All-Statistics-Statistical-Inference-Springer/dp/1441923225">All of Statistics by Wasserman</a></p></li>
<li><p><a class="reference external" href="https://github.com/cranmer/PRML">PRML</a></p></li>
<li><p><a class="reference external" href="https://mml-book.github.io">Mathematics for Machine Learning</a></p></li>
<li><p><a class="reference external" href="https://mitpress.mit.edu/books/elements-causal-inference">Elements of Causal Inference by Jonas Peters, Dominik Janzing and Bernhard Schölkopf</a> <a class="reference external" href="https://www.dropbox.com/s/dl/gkmsow492w3oolt/11283.pdf">free PDF</a></p></li>
<li><p><a class="reference external" href="https://web.stanford.edu/~hastie/ElemStatLearn//">Trevor Hastie, Rob Tibshirani, and Jerry Friedman, Elements of Statistical Learning, Second Edition, Springer, 2009</a></p></li>
</ol>
</div>
<div class="section" id="influential-texts">
<h2>Influential texts<a class="headerlink" href="#influential-texts" title="Permalink to this headline">¶</a></h2>
<ol class="simple">
<li><p><a class="reference external" href="https://micromath.wordpress.com/2008/04/14/donald-knuth-calculus-via-o-notation/">Knuth Calculus</a></p></li>
<li><p><a class="reference external" href="https://mitpress.mit.edu/books/functional-differential-geometry">Functional Differential Geometry by Gerald Jay Sussman and Jack Wisdom</a></p></li>
</ol>
</div>
<div class="section" id="misc">
<h2>Misc<a class="headerlink" href="#misc" title="Permalink to this headline">¶</a></h2>
<ol class="simple">
<li><p><a class="reference external" href="https://dwh.gg/NeurIPSastro">NeurIPS astro tutorial with datasets etc.</a></p></li>
<li><p><a class="reference external" href="https://arxiv.org/abs/2012.09874">Paper about statistical combinations from phys/astro authors</a></p></li>
<li><p><a class="reference external" href="https://www.kaggle.com/borisettinger/gentle-introduction-to-automatic-differentiation">Gentle Introduction to Automatic Differentiation on Kaggle</a></p></li>
<li><p><a class="reference external" href="https://danilorezende.com/wp-content/uploads/2018/07/divergences.pdf">Short notes on divergence measures by Danilo Rezende</a></p></li>
<li><p><a class="reference external" href="http://www.stat.yale.edu/~yw562/teaching/it-stats.pdf">Lecture notes on: Information-theoretic methods for high-dimensional statistics, by Yihong Wu</a></p></li>
</ol>
</div>
<div class="section" id="meta">
<h2>Meta<a class="headerlink" href="#meta" title="Permalink to this headline">¶</a></h2>
<blockquote class="twitter-tweet"><p lang="en" dir="ltr">The 10 most helpful *free1. online machine learning courses, via <a href="https://twitter.com/chipro?ref_src=twsrc%5Etfw">@chipro</a><br><br>Full thread: <a href="https://t.co/RUcG2AL1uC">https://t.co/RUcG2AL1uC</a><a href="https://twitter.com/hashtag/MondayMotivation?src=hash&ref_src=twsrc%5Etfw">#MondayMotivation</a> <a href="https://t.co/Fd3sN2u7UV">pic.twitter.com/Fd3sN2u7UV</a></p>— MIT CSAIL (@MIT_CSAIL) <a href="https://twitter.com/MIT_CSAIL/status/1295391687783718914?ref_src=twsrc%5Etfw">August 17, 2020</a></blockquote> <script async src="https://platform.twitter.com/widgets.js" charset="utf-8"></script>
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