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Machine learning 101 sit hvr

Basic introduction to machine learning as presented at SAP Inside Track Hannover

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Machine learning 101 sit hvr

  1. 1. Machine Learning 101 Fred Verheul
  2. 2. What we won’t cover… • Deep learning / Neural Networks • Specifics of ML-algorithms • Tools / Libraries / Code • SAP Products, like HANA / Predictive Analytics / Vora / … • Ethics, algorithmic transparency & fairness • Hardware 2
  3. 3. Examples: Recommender systems 3
  4. 4. Examples, continued… 4 SPAM- filtering Handwriting recognition
  5. 5. ML in the news: Deepmind’s AlphaGo 5
  6. 6. 6
  7. 7. Machine Learning "Field of study that gives computers the ability to learn without being explicitly programmed” (Arthur Samuel, 1959) 7
  8. 8. What is Machine Learning? 8 Computer Computer Traditional Programming Machine Learning Data Data Program Output Program Output
  9. 9. 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) 9
  10. 10. Basic Machine Learning ‘workflow’ 10 Feature Vectors Training data Labels Machine Learning Algorithm Feature Vectors New data Prediction Training Phase Operational Phase Predictive Model
  11. 11. Training Phase in more detail 11 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’
  12. 12. CRISP-DM: data mining process 12 ML important ML important
  13. 13. Examples of ML tasks Supervised learning Regression  target is numeric Classification  target is categorical 13 Unsupervised learning Clustering Dimensionality reduction
  14. 14. Modeling: so many algorithms… 14
  15. 15. ML Algorithms: by Representation Collection of candidate models/programs, aka hypothesis space 15 Decision trees Instance-based Neural networks Model ensembles
  16. 16. ML Algorithms: by Evaluation Evaluation: Quality measure for a model 16 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)
  17. 17. Optimization: how the algorithm ‘learns’, depends on representation and evaluation ML Algorithms: by Optimization 17 Greedy Search, ex. of combinatorial optimization Gradient Descent (or in general: Convex Optimization) Linear Programming (or in general: Constrained/Nonlinear Optimization)
  18. 18. Training error vs test error 18
  19. 19. 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 19
  20. 20. Take-aways • Goal of ML: generalize from training data (not optimization!!) • Part of ‘Data Mining Process’, not a goal in and of itself • No magic! Just some clever algorithms… • Increasingly important non-technical aspects: • Ethics • Algorithmic transparency 20
  21. 21. Thank You www.soapeople.com info@soapeople.com @SOAPEOPLE Fred Verheul Big Data Consultant +31 6 3919 2986 fred.verheul@soapeople.com @fredverheul

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  • soccernation

    Jul. 9, 2021

Basic introduction to machine learning as presented at SAP Inside Track Hannover

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