machine learning 101 dkom 2017
TRANSCRIPT
Machine Learning 101
Fred Verheul
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Machine Learning
"Field of study that gives computers the ability to learnwithout being explicitly programmed” (Arthur Samuel, 1959)
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What is Machine Learning?
Computer
Computer
Traditional Programming
Machine Learning
Data
Data
Program Output
ProgramOutput
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Prediction is hard…
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Sweet spot for Machine Learning
• It’s impossible to write down the rules in code:• Too many rules• Too many factors influencing the rules• Too finely tuned• We just don’t know the rules (image recognition)
• Lots of labeled data (examples) available (e.g. historical data)
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Basic Machine Learning ‘workflow’
Feature Vectors
Training data
Labels
Machine Learning Algorithm
Feature Vectors
New data Prediction
Training Phase
Operational Phase
Predictive Model
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Training Phase in more detail
Raw dataData
preparation Feature Vectors
Training Data
Test data
Model Building (by ML
algorithm)
Model Evaluation
Predictive Model
Feedback loop
data cleansingdata transformation
normalizationfeature extraction
aka ‘learning’
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Examples of ML tasksSupervised learning
Regression target is numeric
Classification target is categorical
Unsupervised learning
Clustering
Dimensionalityreduction
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Modeling: so many algorithms…
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ML Algorithms: by RepresentationCollection of candidate models/programs, aka hypothesis space
Decision trees
Instance-based
Neural networks
Model ensembles
ML Algorithms: by Evaluation
Evaluation: Quality measure for a model
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Regression
Example metric: Root Mean Squared Error
RMSE =
Binary classification: confusion matrix
Accuracy: 8 + 971 -> 97,9%
Example: medical test for a disease
Accuracy: Better evaluation metrics:• Precision: 8 / (8 + 19)• Recall: 8 / (8 + 2)
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Optimization: how the algorithm ‘learns’, depends on representation and evaluation
ML Algorithms: by Optimization
Greedy Search, ex. of combinatorial optimization
Gradient Descent (or in general: Convex Optimization)
Linear Programming (or in general:Constrained/Nonlinear Optimization)
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Training error vs test error
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Data Science for Business
• Focuses more on general principles than specific algorithms
• Not math-heavy, does contain some math
• O’Reilly link: http://shop.oreilly.com/product/0636920028918.do
• Book website: http://data-science-for-biz.com/DSB/Home.html
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What has NOT been covered (1)
• Deep learning / Neural Networks
• Covered in other presentations at DKOM
• Also recommended for further reading (deep dive):• http://neuralnetworksanddeeplearning.com/index.html
• Specifics of ML-algorithms
• All over the internet… e.g. at http://machinelearningmastery.com/
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What has NOT been covered (2)
• Libraries (examples):• Tensorflow, Caffe, Theano, Keras• SciPy & scikit-learn• Spark MLLib (Scala/Java/Python)
• Programming languages:
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What has NOT been covered (3)
• SAP products:
• SAP HANA, SAP HANA Vora, SAP BO Predictive Analytics(!), HCP Predictive Services
• New machine learning platform
• Hardware
• Nvidia talk about GPUs
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What has NOT been covered (4)
• Ethics and algorithmic transparency:
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What has NOT been covered (5)
• The Data Science & Data Mining Process:
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What has NOT been covered (6)
• How to integrate ML into your business application
• I hope SAP is figuring that out as we speak ;-)
• Have a look at SAP Predictive Analytics Integrator
• https://help.sap.com/pai
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Take-aways
• Goal of ML: generalize from training data (not optimization!!)
• No magic! Just some clever algorithms…
• Increasingly important non-technical aspects:• Ethics
• Algorithmic transparency
