Splitting the 3 topics into their own sub-headings

vishweshs4 · Nov 4, 2019 · 6f5ad21 · 6f5ad21
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 # Mathematics, Statistics, Probability & Probabilistic programming
 
+## Mathematics
+
 - [Mathematics for Machine Learning](https://mml-book.github.io/book/mml-book.pdf)
+- [Topic-wise notes: maths & stats](https://www.ctanujit.org/notes.html)
+- [Number Representation Systems Explained in One Picture](https://www.datasciencecentral.com/profiles/blogs/number-representation-systems-explained-in-one-picture)
+- Abductive Learning: Towards Bridging Machine Learning and Logical Reasoning: [Slides](http://daiwz.net/org/slides/ABL-meetup.html) | [Video](https://www.youtube.com/watch?v=ETHrFxiFIUM) | [GitHub](https://github.com/AbductiveLearning/ABL-HED)
+
+## Statistics
+
 - [Statistics for Machine learning (paid book: Packt Publishing)](https://www.packtpub.com/big-data-and-business-intelligence/statistics-machine-learning)
 - [Topic-wise notes: maths & stats](https://www.ctanujit.org/notes.html)
 - [5 Lesson 5 Measures Of Skewness And Kurtosis](https://sol.du.ac.in/mod/book/view.php?chapterid=1067&id=1317)
 - [Data Types in Statistics](https://towardsdatascience.com/data-types-in-statistics-347e152e8bee)
 - [An Introduction To Statistical Learning with Applications in R](https://github.com/tpn/pdfs/blob/master/An%20Introduction%20To%20Statistical%20Learning%20with%20Applications%20in%20R%20(ISLR%20Sixth%20Printing).pdf)
-- [Number Representation Systems Explained in One Picture](https://www.datasciencecentral.com/profiles/blogs/number-representation-systems-explained-in-one-picture)
 - [Fractional Exponentials - Dataset to Benchmark Statistical Tests](https://www.datasciencecentral.com/profiles/blogs/weird-mathematical-object-fractional-exponential)
-- [Bayesian active learning with Gaussian processes](https://bitbucket.org/JohnReid/2019-bayesian-mixer/raw/5439d0bf0be2d01dc2d95ab89407211a875021ae/Bayesian-Mixer.pdf) | [source code](https://github.com/JohnReid/dynlearn) | [John Reid](http://johnreid.github.io/)
 - [Amortized Monte Carlo Integration](https://www.youtube.com/watch?v=-oHCqLFLTAI) by [Tom Rainforth](http://www.robots.ox.ac.uk/~twgr/)
 - [Kernel Embeddings, Meta Learning & Distributional Transfer](https://www.youtube.com/watch?v=vjG-2RjHnAA) by [Dino Sejdinovic](http://www.stats.ox.ac.uk/~sejdinov/)
-- [Probabilistic Symmetry and Invariant Neural Networks](https://www.youtube.com/watch?v=u8Jt1HkWTn4) by [Benjamin Bloem-Reddy](https://www.stat.ubc.ca/~benbr/)
-- [Bayesian nonparametric ML through randomized loss functions & posterior bootstraps](https://www.youtube.com/watch?v=y_gI9R4Oe0g) by [Chris Holmes](http://www.stats.ox.ac.uk/~cholmes/)
-- [Skillsmatter: Precision Medicine With Mechanistic, Bayesian Models](https://skillsmatter.com/skillscasts/12129-bayesian-mixer-london-june)
-- [Chris Fonnesbeck’s presentation: PyMC's Big Adventure - Lessons Learned from the Development of Open-source Software for Probabilistic Programming](https://gitpitch.com/fonnesbeck/neurips_2018_talk#/) | [Chris Fonnesbeck](https://twitter.com/fonnesbeck)
-- [Colin Carroll’s presentation: Tidy and beautiful - Visualizing Bayesian models with xarray and ArviZ](https://colcarroll.github.io/arviz_pydata_nyc/#/) | [Colin Carroll](https://twitter.com/colindcarroll)
 - [Thomas Wiecki’s presentation: Machine Learning and Statistics - don't mind the gap](https://docs.google.com/presentation/d/1buknIrG5b8u0twrwvlxcTudIOdx68AlqDiST_A_jJ9g/edit#slide=id.g3dc76d9ec1_0_6) | [Thomas Wiecki](https://twitter.com/twiecki)
 - [Interactive Machine Learning, Deep Learning and Statistics websites](https://p.migdal.pl/interactive-machine-learning-list/)
-- [Visualization in Bayesian workflow](https://arxiv.org/abs/1709.01449)
-- [Suite of probabilitic programming language repos from Improbable.io](https://github.com/improbable-research)
 - [G. James, D. Witten et al., An Introduction to Statistical Learning with Applications in R](http://www-bcf.usc.edu/~gareth/ISL/)
 - [Static and dynamic network visualization with R - Katya Ognyanova](http://kateto.net/network-visualization)
+- [Learning from Data: the art of statistics](http://www.lse.ac.uk/Events/2019/03/20190327t1830vHKT/Learning-from-Data) | [The Art of Statistics: Learning from Data by David Spiegelhalter](https://www.amazon.com/Art-Statistics-Learning-Pelican-Books-ebook/dp/B07HQDJD99)
+- [Statistical Rethinking](https://issuu.com/biwugrok17/docs/pdf_download_online_pdf_statistical)
+- Abductive Learning: Towards Bridging Machine Learning and Logical Reasoning: [Slides](http://daiwz.net/org/slides/ABL-meetup.html) | [Video](https://www.youtube.com/watch?v=ETHrFxiFIUM) | [GitHub](https://github.com/AbductiveLearning/ABL-HED)
+- [Statistics by Chris Albon](https://chrisalbon.com/#statistics) - covering Frequentist topics
+- [See Data > Statistics section more related links](../data/README.md#statistics)
+
+## Probability and Probabilistic programming
+- [Bayesian active learning with Gaussian processes](https://bitbucket.org/JohnReid/2019-bayesian-mixer/raw/5439d0bf0be2d01dc2d95ab89407211a875021ae/Bayesian-Mixer.pdf) | [source code](https://github.com/JohnReid/dynlearn) | [John Reid](http://johnreid.github.io/)
 - [Probabilistic Programming & Bayesian Methods for Hackers - Cam Davidson-Pilon](http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/)
+- [Probabilistic Symmetry and Invariant Neural Networks](https://www.youtube.com/watch?v=u8Jt1HkWTn4) by [Benjamin Bloem-Reddy](https://www.stat.ubc.ca/~benbr/)
+- [Bayesian nonparametric ML through randomized loss functions & posterior bootstraps](https://www.youtube.com/watch?v=y_gI9R4Oe0g) by [Chris Holmes](http://www.stats.ox.ac.uk/~cholmes/)
 - [A visual guide to Bayesian thinking](https://www.youtube.com/watch?v=BrK7X_XlGB8)
 - [Practical Probabilistic Programming book (pdf)](http://www.unquotebooks.com/download/practical-probabilistic-programming/)
-- [Learning from Data: the art of statistics](http://www.lse.ac.uk/Events/2019/03/20190327t1830vHKT/Learning-from-Data) | [The Art of Statistics: Learning from Data by David Spiegelhalter](https://www.amazon.com/Art-Statistics-Learning-Pelican-Books-ebook/dp/B07HQDJD99)
+- [Visualization in Bayesian workflow](https://arxiv.org/abs/1709.01449)
+- [Suite of probabilitic programming language repos from Improbable.io](https://github.com/improbable-research)
+- [Chris Fonnesbeck’s presentation: PyMC's Big Adventure - Lessons Learned from the Development of Open-source Software for Probabilistic Programming](https://gitpitch.com/fonnesbeck/neurips_2018_talk#/) | [Chris Fonnesbeck](https://twitter.com/fonnesbeck)
+- [Skillsmatter: Precision Medicine With Mechanistic, Bayesian Models](https://skillsmatter.com/skillscasts/12129-bayesian-mixer-london-june)
+- [Colin Carroll’s presentation: Tidy and beautiful - Visualizing Bayesian models with xarray and ArviZ](https://colcarroll.github.io/arviz_pydata_nyc/#/) | [Colin Carroll](https://twitter.com/colindcarroll)
+- [Amortized Monte Carlo Integration](https://www.youtube.com/watch?v=-oHCqLFLTAI) by [Tom Rainforth](http://www.robots.ox.ac.uk/~twgr/)
 - Books
   - [Bayesian Data Analysis Third Edition[Gelman]](https://www.academia.edu/32086149/Bayesian_Data_Analysis_Third_Edition_Gelman_.pdf)
-  - [Statistical Rethinking](https://issuu.com/biwugrok17/docs/pdf_download_online_pdf_statistical)
   - [Think Bayesian](http://greenteapress.com/wp/think-bayes)
   - [Think Stats, 2nd edition](https://greenteapress.com/wp/think-stats-2e/) | [github](https://github.com/AllenDowney/ThinkStats2) - is an introduction to Probability and Statistics for Python programmers
-  - [Mathematics for Machine Learning](https://mml-book.github.io/)
   - [Model Based Machine Learning Book](http://www.mbmlbook.com/)
   - [Bayesian Analysis with Python: Introduction to statistical modeling and probabilistic programming using PyMC3 and ArviZ](https://www.amazon.com/Bayesian-Analysis-Python-Introduction-probabilistic/dp/1789341655)
 - [Probability Learning I : Bayes’ Theorem](https://towardsdatascience.com/probability-learning-i-bayes-theorem-708a4c02909a)
 - [Probability Learning II: How Bayes’ Theorem is applied in Machine Learning](https://towardsdatascience.com/probability-learning-ii-how-bayes-theorem-is-applied-in-machine-learning-bd747a960962)
 - [Learning & Reasoning in Artificial Intelligence](https://www.youtube.com/watch?v=K_GOHepjY2o) by [Thomas Lukasiewicz](http://www.cs.ox.ac.uk/thomas.lukasiewicz/)
 - Abductive Learning: Towards Bridging Machine Learning and Logical Reasoning: [Slides](http://daiwz.net/org/slides/ABL-meetup.html) | [Video](https://www.youtube.com/watch?v=ETHrFxiFIUM) | [GitHub](https://github.com/AbductiveLearning/ABL-HED)
-- [Coursera Course: Probability and distribution](
-https://media.licdn.com/dms/document/C511FAQGFKgIKuW_EEA/feedshare-document-pdf-analyzed/0?e=1571785200&v=beta&t=XyEEqUgi3y4L1hiZ7CxlxbAXyZmM_zcCCdn-Lr04ns8)
-- [Statistics by Chris Albon](https://chrisalbon.com/#statistics) - covering Frequentist topics
-- [See Data > Statistics section more related links](./data/README.md#statistics)
+- [Coursera Course: Probability and distribution](https://media.licdn.com/dms/document/C511FAQGFKgIKuW_EEA/feedshare-document-pdf-analyzed/0?e=1571785200&v=beta&t=XyEEqUgi3y4L1hiZ7CxlxbAXyZmM_zcCCdn-Lr04ns8)
+
 
 # Contributing