[DSS Tutorials] Updated the Readme files for DSS tutorials.

Author: srikris

README.md

@@ -15,3 +15,4 @@ If you'd like to run it, you may register for GraphLab Create
 - Webinars[[Notebooks]](webinars/README.md)
 - Strata + Hadoop World, New York City 2015 [[Event Page]](http://strataconf.com/big-data-conference-ny-2015/public/schedule/detail/43217) [[Tutorials]](strata-nyc-2015/README.md)
 - Strata + Hadoop World, San Jose, 2016 [[Event Page]](http://conferences.oreilly.com/strata/hadoop-big-data-ca/public/schedule/detail/47056).
+- Data Scince Summit, San Francisco, 2016 

dss-2016/churn_prediction/README.md

@@ -0,0 +1,22 @@
+Churn Prediction 
+=================
+
+User-activity data records typically contain user actions performed on a
+ website, service, or product. These user activity transactions tell us a lot
+ about the user's current interests & preferences. Knowing these interests and
+ preferences can help make businesses make better decisions. 
+
+ Recommender systems, fraud detection, churn prediction, and lead scoring are
+ examples of data products that can require user-activity data. In this
+ two-part tutorial, you will first learn how to work with user activity data
+ and then learn about two specific examples of applications that can leverage
+ user activity data; churn prediction & lead scoring;
+
+ Churn prediction is the task of identifying users that are likely to stop
+ using a service, product or website. Lead scoring is the task of prioritizing
+ users based on the probability that are likely to start using a service,
+ product or website. In the first part of the tutorial, you will learn to:
+ - Train a model to forecast user churn
+ - Explore & Evaluate predictions made by the model
+ - Consume predictions made by the model in an external application
+

dss-2016/lead_scoring/README.md

@@ -0,0 +1,14 @@
+Lead Scoring
+============
+
+Prioritizing new leads is critical for sales and marketing teams. Modern
+machine learning methods use historical data and state of the art classifiers
+to learn a probabilistic relationship between sales account features and
+conversion outcome, enabling us to predict with very high accuracy which open
+accounts are likely to convert. This improvement in accuracy translates
+directly into more efficient use of sales and marketing resources.
+
+In this tutorial, I'll discuss the major themes in predictive lead scoring and
+walk through Python code for building a lead scoring application. The demo uses
+Turi's new Lead Scoring Toolkit, but the focus is on the design and
+implementation principles for the lead scoring task.

dss-2016/recommendation_systems/README.md

@@ -1,23 +1,13 @@
 # Data Science Summit 2016 
 
-This directory contains demo notebooks used for the "Introduction to Recommender Systems", the second session of **Machine Learning 101**.
+Countless online services use recommender systems to provide personalization to
+their users. This is important for selling related items, increasing user
+engagement, and so on. 
 
-In this session we 
+In this tutorial, you will learn
+- the key machine learning concepts that underpin most modern recommender systems
+- how to build your own recommender system using off-the-shelf tools
+- the strengths and weaknesses of collaborative filtering and content-based
+  approaches, as well as hybrid methods
+- how to explore, explain, and evaluate your recommender models
 
-- give an introduction to recommendation systems, 
-- show how easy it is to get started
-- provide examples and slides
-
-Along the way, we also cover feature engineering and deploying machine learning models as a predictive service.
-
-## Setup Instructions
-
-You can browse the notebooks using Github IPython notebook viewer. Note that some images may not be rendered correctly. If you'd like to run it, follow these steps to set up your machine.
-
-- [Download](https://turi.com/download/) GraphLab Create and then follow instructions to [install](https://turi.com/download/install.html).
-- Download and unzip the datasets 
-
-## Handy references
-
-- [GraphLab Create User Guide](https://turi.com/learn/userguide)
-- [Turi Forum](http://forum.turi.com/categories/graphlab-create)