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Machine learning 101 dkom 2017

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Basic intro into Machine Learning for SAP developers, given at DKOM 2017

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Machine learning 101 dkom 2017

  1. 1. Machine Learning 101 Fred Verheul
  2. 2. Machine Learning "Field of study that gives computers the ability to learn without being explicitly programmed” (Arthur Samuel, 1959) 2
  3. 3. What is Machine Learning? 3 Computer Computer Traditional Programming Machine Learning Data Data Program Output Program Output
  4. 4. Prediction is hard… 4
  5. 5. 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) 5
  6. 6. Basic Machine Learning ‘workflow’ 6 Feature Vectors Training data Labels Machine Learning Algorithm Feature Vectors New data Prediction Training Phase Operational Phase Predictive Model
  7. 7. Training Phase in more detail 7 Raw data Data preparation Feature Vectors Training Data Test data Model Building (by ML algorithm) Model Evaluation Predictive Model Feedback loop data cleansing data transformation normalization feature extraction aka ‘learning’
  8. 8. Examples of ML tasks Supervised learning Regression  target is numeric Classification  target is categorical 8 Unsupervised learning Clustering Dimensionality reduction
  9. 9. Modeling: so many algorithms… 9
  10. 10. ML Algorithms: by Representation Collection of candidate models/programs, aka hypothesis space 10 Decision trees Instance-based Neural networks Model ensembles
  11. 11. ML Algorithms: by Evaluation Evaluation: Quality measure for a model 11 Regression Example metric: Root Mean Squared Error RMSE = Binary classification: confusion matrix Accuracy: 8 + 971 -> 97,9% Example: medical test for a disease Positive Negative P True positives TP False Negatives FN N False positives FP True Negatives TN True Class Predicted class Accuracy: Better evaluation metrics: • Precision: 8 / (8 + 19) • Recall: 8 / (8 + 2)
  12. 12. Optimization: how the algorithm ‘learns’, depends on representation and evaluation ML Algorithms: by Optimization 12 Greedy Search, ex. of combinatorial optimization Gradient Descent (or in general: Convex Optimization) Linear Programming (or in general: Constrained/Nonlinear Optimization)
  13. 13. Training error vs test error 13
  14. 14. 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/063692 0028918.do • Book website: http://data-science-for- biz.com/DSB/Home.html 14
  15. 15. 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/ 15
  16. 16. What has NOT been covered (2) • Libraries (examples): • Tensorflow, Caffe, Theano, Keras • SciPy & scikit-learn • Spark MLLib (Scala/Java/Python) • Programming languages: 16
  17. 17. 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 17
  18. 18. What has NOT been covered (4) • Ethics and algorithmic transparency: 18
  19. 19. What has NOT been covered (5) • The Data Science & Data Mining Process: 19
  20. 20. 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 20
  21. 21. 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 21
  22. 22. Thank You www.soapeople.com info@soapeople.com @SOAPEOPLE Fred Verheul Big Data Consultant +31 6 3919 2986 fred.verheul@soapeople.com

Basic intro into Machine Learning for SAP developers, given at DKOM 2017

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