# Machine Learning textbook with a Bayesian Perceptive

**URL:** <https://discourse.pymc.io/t/machine-learning-textbook-with-a-bayesian-perceptive/68>\
**Category:** Sharing\
**Tags:** books\
**Created:** [June 14, 2017, 9:42am UTC](https://discourse.pymc.io/t/machine-learning-textbook-with-a-bayesian-perceptive/68 "2017-06-14T09:42:47Z")\
**Posts on this page:** 6\
**Page:** 1

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**Author:** ![junpenglao](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/junpenglao/32/8_2.png) [@junpenglao](https://discourse.pymc.io/u/junpenglao)\
**Post date:** [June 14, 2017, 9:42am UTC](https://discourse.pymc.io/t/machine-learning-textbook-with-a-bayesian-perceptive/68/1 "2017-06-14T09:42:47Z")

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We had a discussion on the pymc\_devs channel about what books to recommend to people wants to learn Machine Learning from a Bayesian perspective. I think it is worth to share:

To me, the classic Christopher Bishop is a must read:

> **[Pattern Recognition and Machine Learning | Christopher Bishop | Springer](https://www.springer.com/us/book/9780387310732)**
>
> Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past...

@colcarroll and @fonnesbeck recommended Murphy along with Hastie & Tibshirani:

> **[Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)](https://www.amazon.com/Machine-Learning-Probabilistic-Perspective-Computation/dp/0262018020/ref=sr_1_2?ie=UTF8&qid=1336857747&sr=8-2)**
>
> 4.3 out of 5 stars, 
>   
>   
>   
> $90.00

  
[http://www.cs.ubc.ca/~murphyk/MLbook/](http://www.cs.ubc.ca/~murphyk/MLbook/)

> **[The Elements of Statistical Learning - Data Mining, Inference, and...](https://www.springer.com/de/book/9780387848570)**
>
> This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many...

  
[http://statweb.stanford.edu/~tibs/ElemStatLearn/](http://statweb.stanford.edu/~tibs/ElemStatLearn/)

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**Author:** ![pksrijith](https://avatars.discourse-cdn.com/v4/letter/p/3ab097/32.png) [@pksrijith](https://discourse.pymc.io/u/pksrijith)\
**Post date:** [June 17, 2017, 1:43pm UTC](https://discourse.pymc.io/t/machine-learning-textbook-with-a-bayesian-perceptive/68/2 "2017-06-17T13:43:21Z")

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Bayesian Data Analysis, by Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin is another good read  
[http://www.stat.columbia.edu/~gelman/book/](http://www.stat.columbia.edu/~gelman/book/)

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**Author:** ![dovgalec](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/dovgalec/32/73_2.png) [@dovgalec](https://discourse.pymc.io/u/dovgalec)\
**Post date:** [July 6, 2017, 4:45pm UTC](https://discourse.pymc.io/t/machine-learning-textbook-with-a-bayesian-perceptive/68/3 "2017-07-06T16:45:09Z")

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The “puppy book” by John Kruschke is a good read, even for un-initiated. "Doing Bayesian Data Analysis"  
[https://sites.google.com/site/doingbayesiandataanalysis/](https://sites.google.com/site/doingbayesiandataanalysis/)

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**Author:** ![AustinRochford](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/austinrochford/32/92_2.png) [@AustinRochford](https://discourse.pymc.io/u/AustinRochford)\
**Post date:** [July 6, 2017, 7:11pm UTC](https://discourse.pymc.io/t/machine-learning-textbook-with-a-bayesian-perceptive/68/4 "2017-07-06T19:11:01Z")

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Murhpy is my go-to reference for most of ML

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**Author:** ![JWarmenhoven](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/jwarmenhoven/32/51_2.png) [@JWarmenhoven](https://discourse.pymc.io/u/JWarmenhoven)\
**Post date:** [July 7, 2017, 7:35am UTC](https://discourse.pymc.io/t/machine-learning-textbook-with-a-bayesian-perceptive/68/5 "2017-07-07T07:35:24Z")

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I read the first few chapters of ‘Statistical Rethinking’ by McElreath and really like his style of writing.

In general I find that reading several authors on the same subject is very helpful to develop understanding.

[https://www.crcpress.com/Statistical-Rethinking-A-Bayesian-Course-with-Examples-in-R-and-Stan/McElreath/p/book/9781482253443](https://www.crcpress.com/Statistical-Rethinking-A-Bayesian-Course-with-Examples-in-R-and-Stan/McElreath/p/book/9781482253443)

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**Author:** ![AustinRochford](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/austinrochford/32/92_2.png) [@AustinRochford](https://discourse.pymc.io/u/AustinRochford)\
**Post date:** [July 10, 2017, 2:57pm UTC](https://discourse.pymc.io/t/machine-learning-textbook-with-a-bayesian-perceptive/68/6 "2017-07-10T14:57:11Z")

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Yes, this book is really great. @aloctavodia has [ported](https://github.com/aloctavodia/Statistical-Rethinking-with-Python-and-PyMC3) much of it to PyMC3.