Getting Started with Machine Learning on Azure
#Seth Juarez

ML on Azure is easy…
…if you understand a few things about machine learning first

Agenda
1. Data Science
2. Prediction
3. Process
4. nuML
5. AzureML
6. Models (if time permits)

Data Science
key word – science – try something, it might work, repeat with a different trial, etc.
Science is inexact
Guess, test, repeat

Machine Learning
finding (and exploiting) patterns in data
replacing “human writing code” with “human supplying data”
the trick of ML is generalization

Supervised v. Unsupervised Learning
Supervised – prediction – we know what we are looking for
Unsupervised – lots of data, try and figure out things from the data (clusters, trends, etc.)

Kinect finding your body is Supervised (knows what it is looking for)
Netflix figuring out recommendations is Unsupervised

What kinds of decision?
binary – yes/no, male/female
multi-class – grades, class
regression – number between 0 and 100, real value

multi-class can be done using binary (A v. something else, B v. something else, etc. – then take the best scoring one at the value)

Process
1. Data
2. Clean and Transform the Data
3. Create a Model
4. Use the Model to predict things

“features” is the term used to describe the attributes that could influence your decision
Data cleaning and transformation is just shaving yaks (it takes a lot of time)

nuML [http://www.numl.net/]
A .Net ML library
Comes with a REPL for C#
Attributes to mark things [Feature], [Label], etc.
gets data into matrix
(Mahout does this stuff in Java)
turns it into a model
has visualizers for the model (graphical and text based)
Can use the model to create a prediction given a set of inputs (features)

How do you know it worked?
Train and Test – use some portion of your data to train the model, and then the rest to test (Seth is suggesting 80-20)
nuML has facilities to do this
It will take your data, create a model, score the model, and repeat 100 times
Then it returns the best model
You have to be careful of overfitting the problem – if you create too fine grained of a model you might overfit the model to your data and get bad predictions.
Training v. Testing means – if you have 10 rows of data, you will train on 8 rows and then test with 2

Limitation – limited amount of resources on your machine (CPU, RAM, Disk)

AzureML
Drag and droppy action to set up the path through the steps above
Will give you some charts of your data so you can start to get some insight into it
Has a bunch of transformation options, also draggy
If you don’t know the 4 step process, the “wizard” can be tricky, but if you know it, it’s fairly straightforward.
You just drag and drop your flow through the steps – you can have multiple transformations (shaving the yak)
You can define the training/testing ratio by specifying the percentage to use for training
Define scoring for the trained model
Evaluate the scored models
You need to define what you are learning so that it can train the model
precision – true positive over true positive plus false positive
accuracy – tp + tn / the whole set

If you are getting 95% accuracy, you probably have something wrong – that is usually too accurate (overfitting). Ideal is in the 80-90% range.

You can run multiple scoring methods – you will get a chart comparing them

You can publish as a web service to use your model for predictions

Linear Classifiers – need to define a boundary between sets
Perceptron algorithm – draws a line between two sets
Kernel Perceptron – takes data that can’t have a line drawn between it, 3D-ifies it, draws a plane between the sets

Lots of math formulas – review the slides

“Kernel Trick” – will allow you to determine a seperator between two sets no matter what space by going into multiple dimensions with the data

If you cannot get a reasonable answer – neural networks
A network of perceptrons
Now we’re in to calculus

Consuming Data with F# Type Providers
#Rachel Reese

What are type providers?
A mechanism to provide types to the compiler – a compiler plugin.
WSDL, CSV, JSON, Other languages (R, Python, Powershell), etc.
As long as it has a schema, you can write a type provider for it

Why?
Simplify consuming data by encapsulating it in a type provider
Facilitates using data from the REPL
Allows you to play with the data in the REPL to figure things out about it

Demos
Showing us how to use existing type providers

F# and Machine Learning – A Winning Combination
#Mathias Brandewinder

A fellow accidental developer with an operations research background!

Data Scientist – person who is better at statistics than any software engineer and better at software engineering than any statistician. (from a tweet)

.Net underrepresented in data science/machine learning because it is perceived (possibly correctly) as not being a very good fit. But F# can be, so we are going to talk about it.

Machine Learning
* Writing a program to perform a task
* More data, better performance
* Not explicitly programmed for this [no code change]

i.e. the program gets better all by itself

Classification and Regression
Classification – using data to classify items (spam v. ham for example)
Regression – predicting a number (price given some attributes for example)
Both are part of the concept Supervised Learning – you know what question you are trying to answer and you use data to fit a predictive model

Support Vector Machine example – classic algorithm – we need to look it up
Using the Accord.Net library

F# has a REPL, which makes it easier to iteratively tweek algorithm type problems. Load up the data set and then keep running different variations of the application against it without reloading. When working with lots of data this can be a big time saver.

Goal is to build a model that will predict new data. Need to build it with “training data”. Take your data set and split it in half. Use half to train the algorithm. Don’t expect your model to be perfect.

Algebra
Math.Net allows you to do algebra in .Net
let A = matrix [ [ 1.; 2.; 3.; ];
[4,; 5.; 6.; ]
[7.; 8.; 9.;]]
Typical in Python, not possible in C#. F# makes the matrix obvious.
Linear Regression problems can be solved in this problem

F# has a timer built in to the REPL, so you can find out how long your functions take to run -> #time;;

Gamers care about algebra – graphics rendering uses vectors
GPUs are optimized for algebra processing
You can use a different LinearAlgebraProvider that uses the GPU for your work, which runs MUCH faster.
Esentially compiles your F# code to native GPU code. F# is good at cross compiling – there is of course an F# to Javascript compiler as well

Interactive experience with a REPL is a huge advantage, and .Net does actually have some decent machine learning libraries available

Unsupervised Machine Learning
You usually have to write your own libraries because a suitable one probably doesn’t exist. As you learn your domain you may need a custom model

Most ML algos
* Read Data
* Transform into Features
* Learn a Model from the Features
* Evaluate Model Quality

Maps to functional
* Read -> Read
* Transform -> Map
* Learn -> Recursion
* Evaluate -> Fold

Unsupervised example – Tell me somthing about my data
Example – Clustering – find groups of “similar” entities
K-Means
Create centroids for the number of expected groups
Move them closer to group averages
Keep going until there is no more change

Implemented the K Means algo in about 20 lines of F# code (Code is on his github repo)

Type Providers
“No data, no learning” – you need to get data into your system or the algorithm is of no use (80-90% of the job is getting and cleaning data)
“Machine learning is a data janitorial job”
Dynamic languages are nice but you don’t find out about data issues until runtime
Static prevents runtime errors but requires “heavy artillery” (ORMs, etc.)
Type Providers are a compromise between the two
Type providers for csv, JSON, SQL, etc. plus customs for special data sets
There is a WorldBankData provider that has various country and economic statistics, which could be useful. It essentially wraps a public API and makes calls over the Internet to obtain the data.
Type Providers can support queries (like SQL/LINQ) as well
There are even type providers to langauges (R, for example)
Allows you to use functions from R in F# (even gives you a bit of intellisense-like functionality to discover the fuctionality)
You can use F# for what it is good at (type providers) and R for what it is good at