README tweaks

README.md

@@ -481,21 +481,27 @@ Tuesday | Thursday
 **Resources:**
 * scikit-learn's documentation on [decision trees](http://scikit-learn.org/stable/modules/tree.html) includes a nice overview of trees as well as tips for proper usage.
 * For a more thorough introduction to decision trees, read section 4.3 (23 pages) of [Introduction to Data Mining](http://www-users.cs.umn.edu/~kumar/dmbook/index.php). (Chapter 4 is available as a free download.)
-* This paper, [The Science of Singing Along](http://www.doc.gold.ac.uk/~mas03dm/papers/PawleyMullensiefen_Singalong_2012.pdf), contains a neat regression tree for predicting the percentage of an audience at a music venue that will sing along to a pop song.
 * If you want to go deep into the different decision tree algorithms, this slide deck contains [A Brief History of Classification and Regression Trees](https://drive.google.com/file/d/0B-BKohKl-jUYQ3RpMEF0OGRUU3RHVGpHY203NFd3Z19Nc1ZF/view).
+* [The Science of Singing Along](http://www.doc.gold.ac.uk/~mas03dm/papers/PawleyMullensiefen_Singalong_2012.pdf) contains a neat regression tree (page 136) for predicting the percentage of an audience at a music venue that will sing along to a pop song.
+* Decision trees are common in the medical field for differential diagnosis, such as this classification tree for [identifying psychosis](http://www.psychcongress.com/sites/naccme.com/files/images/pcn/saundras/psychosis_decision_tree.pdf).
 
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 ### Class 18: Ensembling
 * Finish decision trees lesson ([notebook](notebooks/17_decision_trees.ipynb))
 * Ensembling ([notebook](notebooks/18_ensembling.ipynb))
+    * [Major League Baseball player data](data/hitters.csv) from 1986-87
+    * [Data dictionary](https://cran.r-project.org/web/packages/ISLR/ISLR.pdf) (page 7)
 
 **Resources:**
 * scikit-learn's documentation on [ensemble methods](http://scikit-learn.org/stable/modules/ensemble.html) covers both "averaging methods" (such as bagging and Random Forests) as well as "boosting methods" (such as AdaBoost and Gradient Tree Boosting).
-* For an intuitive explanation of Random Forests, read Edwin Chen's answer to [How do random forests work in layman's terms?](http://www.quora.com/Random-Forests/How-do-random-forests-work-in-laymans-terms/answer/Edwin-Chen-1)
 * MLWave's [Kaggle Ensembling Guide](http://mlwave.com/kaggle-ensembling-guide/) is very thorough and shows the many different ways that ensembling can take place.
 * Browse the excellent [solution paper](https://docs.google.com/viewer?url=https://raw.githubusercontent.com/ChenglongChen/Kaggle_CrowdFlower/master/Doc/Kaggle_CrowdFlower_ChenglongChen.pdf) from the winner of Kaggle's [CrowdFlower competition](https://www.kaggle.com/c/crowdflower-search-relevance) for an example of the work and insight required to win a Kaggle competition.
 * [Interpretable vs Powerful Predictive Models: Why We Need Them Both](https://medium.com/@chris_bour/interpretable-vs-powerful-predictive-models-why-we-need-them-both-990340074979) is a short post on how the tactics useful in a Kaggle competition are not always useful in the real world.
+* [Not Even the People Who Write Algorithms Really Know How They Work](http://www.theatlantic.com/technology/archive/2015/09/not-even-the-people-who-write-algorithms-really-know-how-they-work/406099/) argues that the decreased interpretability of state-of-the-art machine learning models has a negative impact on society.
+* For an intuitive explanation of Random Forests, read Edwin Chen's answer to [How do random forests work in layman's terms?](http://www.quora.com/Random-Forests/How-do-random-forests-work-in-laymans-terms/answer/Edwin-Chen-1)
+* [Large Scale Decision Forests: Lessons Learned](http://blog.siftscience.com/blog/2015/large-scale-decision-forests-lessons-learned) is an excellent post from Sift Science about their custom implementation of Random Forests.
+* [Understanding Random Forests: From Theory to Practice](http://arxiv.org/pdf/1407.7502v3.pdf) is an in-depth academic analysis of Random Forests, including details of its implementation in scikit-learn.
 
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