azure machine learning intro
TRANSCRIPT
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#AzureML[Olivia Klose]
[Damir Dobric]
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Big Data & MachineLearning
Olivia Klose
20.Okt
01.Dec 08.Dec 15.Dec 22.Dec
12.Jan
From 15.00-18.00 h
Microsoft Office
Bad Homburg
03.Nov 10.Nov 17.Nov 24.Nov
Software EngineeringWS-2014-2015
29.Dec
https://www.yammer.com/uni-se-ws-2014-2015
13.Okt 27.Okt
19.Jan 26.Jan
02.Feb 09.Feb
Azure Machine Learning
Damir Dobric
Olivia Klose
Blog Twitter
Why Learn?1. Learn it when you can’t code it
(e.g. Recognizing Speech/image/gestures)
2. Learn it when you can’t scale it (e.g. Recommendations, Spam & Fraud detection)
3. Learn it when you have to adapt/personalize (e.g. Predictive typing)
4. Learn it when you can’t track it (e.g. AI gaming, robot control)
Can Machine Learning Help Me?• Automated prediction is core
• Lots of past data already
available
• Magic numbers in current
prediction system
• Prediction is small part of
experience
• No past data available
• Many business-rules govern the
experience
• Predictions do not have a
predictable patternYe
s
No
Finding patterns in data
X Y
1 FALSE
9 TRUE
6 TRUE
4 FALSE
8 TRUE
2 FALSE
7 TRUE
3 FALSE
4 FALSE
5 FALSE
10 TRUE
15 TRUE
12 TRUE
X Y
1 FALSE
2 FALSE
3 FALSE
4 FALSE
4 FALSE
5 FALSE
6 TRUE
7 TRUE
8 TRUE
9 TRUE
10 TRUE
12 TRUE
15 TRUE
Writing code
X Y
1 FALSE
2 FALSE
3 FALSE
4 FALSE
4 FALSE
5 FALSE
6 TRUE
7 TRUE
8 TRUE
9 TRUE
10 TRUE
12 TRUE
15 TRUE
public bool Y(X feature){
if(feature > 5)return true;
else return false;
}
public bool Y(X feature){
return feature > 5;}
Supplying data
X Y1 FALSE2 FALSE3 FALSE4 FALSE4 FALSE5 FALSE6 TRUE7 TRUE8 TRUE9 TRUE
10 TRUE12 TRUE15 TRUE
Decisions
Binary classificationTrue/false, male/female, high/low, black/white
Multi-class classification{1,2,3,4}, {A,B,C,D}, {0,1€, 0,5€,1€, 2€}
Regression1,0-100,00, any real value.
Machine Learning approaches
supervised learningpredicting the future
learn from past examples to predict future
unsupervised learningunderstanding the past
making sense of data
learning structure of data
compressing data for consumption
Supervised Learning
• Classification
• Regression
• Recommendation
• Anomaly Detection
Unsupervised Learning
• Clustering
• Principal Component Analysis
Supervised vs. Unsupervised
Supervised or Unsupervised?Classification or Regression?
You have a large inventory of identical items. You want to predict how many of these items will sell over the next 3 months.
Given email labeled as spam/not spam, learn a spam filter.
Given a set of news articles found on the web, group them into set of articles about the same story.
Supervised or Unsupervised?Classification or Regression?
You’d like software to examine individual customer accounts, and for each account decide if it has been hacked/compromised.
Given a database of customer data, automatically discover market segments and group customers into different market segments.
Given a dataset of patients diagnosed as either having diabetes or not, learn to classify new patients as having diabetes or not.
Truth
true false
Guess
positive
𝑡𝑟𝑢𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑓𝑎𝑙𝑠𝑒 𝑝𝑜𝑠𝑖𝑡𝑖𝑣𝑒 𝑝𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 =𝑡𝑝
𝑡𝑝 + 𝑓𝑝
negative
𝑓𝑎𝑙𝑠𝑒 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒 𝑡𝑟𝑢𝑒 𝑛𝑒𝑔𝑎𝑡𝑖𝑣𝑒
𝑟𝑒𝑐𝑎𝑙𝑙 =𝑡𝑝
𝑡𝑝 + 𝑓𝑛𝑎𝑐𝑐𝑢𝑟𝑎𝑐𝑦 =
𝑡𝑝 + 𝑡𝑛
𝑡𝑝 + 𝑡𝑛 + 𝑓𝑝 + 𝑓𝑛
Confusion Matrix
Good to know
Mean
Median separates the higher half of a data from the lower half:
{3, 3, 5, 9, 11} =>5
{3, 5, 7, 9}=>(5 + 7) / 2 = 6
Variance where
Standard DeviationSquare root of the squared variance
Delivering Advanced Analytics Business users access results from anywhere, on any device
• HDInsight• SQL Server VM• SQL DB• Blobs & Tables
Devices Applications Dashboards
Data Microsoft Azure Machine Learning
Storage space
Integrated development environment for
Machine Learning
ML
Studio
Business challenge Business valueModeling Deployment
• Desktop files
• Excel spreadsheet
• Other data files on PC
Cloud
Local
Data to model to web services in minutes
http://studio.azure
ml.net
Web
Clients
API
Model is now a web svc
Monetize this API
Why Azure ML - RecapModel Evaluation
Easy to test and compare different models. (+)
Powerful toolset to deal with data.(+)
Bad integration of custom tooling (exception of R modules) (-)
Web Service APIResults can be easily published as Web Service (+)
Easy to consume a model as REST WS-API (+)
Cannot download as API (-)
Rignt now no support for CORS. (-)
ReferencesMachine Learning
Yaser S. Abu-Mostafa - California Institute of Technology Caltech: https://work.caltech.edu/
Caltech Course of ML: http://www.youtube.com/watch?v=MEG35RDD7RA&list=PLD63A284B7615313A
Stanford video or Coursera Course
Azure ML Intro Damir DobricEpisode I, Episode II
ML Blog: http://blogs.technet.com/b/machinelearning/
Azure ML Getting Started Video
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