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- 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
- 5. ML in the news: Deepmind’s AlphaGo 5
- 6. 6
- 7. Machine Learning "Field of study that gives computers the ability to learn without being explicitly programmed” (Arthur Samuel, 1959) 7
- 8. What is Machine Learning? 8 Computer Computer Traditional Programming Machine Learning Data Data Program Output Program Output
- 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. 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. 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. CRISP-DM: data mining process 12 ML important ML important
- 13. Examples of ML tasks Supervised learning Regression target is numeric Classification target is categorical 13 Unsupervised learning Clustering Dimensionality reduction
- 14. Modeling: so many algorithms… 14
- 15. ML Algorithms: by Representation Collection of candidate models/programs, aka hypothesis space 15 Decision trees Instance-based Neural networks Model ensembles
- 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. 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. Training error vs test error 18
- 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. 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. Thank You www.soapeople.com info@soapeople.com @SOAPEOPLE Fred Verheul Big Data Consultant +31 6 3919 2986 fred.verheul@soapeople.com @fredverheul
Editor's Notes
- Source for images: http://www.havlena.net/en/machine-learning/machine-learning-what-is-it-where-to-learn-about-it/
- Go (DeepMind’s AlphaGo). How it works: https://www.tastehit.com/blog/google-deepmind-alphago-how-it-works/ Go is very different to Chess (DeepBlue 1996). Chess works with a game tree + sophisticated evaluation function. Go is too complex, and there are no good evaluation functions, because Go positions are harder to evaluate. Enter Monte Carlo Tree Search: simulation. Exploration/exploitation trade-off! No Go-knowledge required!
- This diagram is attributed to Pedro Domingos who used it in his Coursera Machine Learning course in 2012.
- Source: https://en.wikipedia.org/wiki/Cross_Industry_Standard_Process_for_Data_Mining
- Sources: Regression - http://gerardnico.com/wiki/data_mining/linear_regression Classification - ?? Clustering - https://en.wikipedia.org/wiki/Cluster_analysis Dimensionality reduction: http://www.sthda.com/english/wiki/factoextra-r-package-easy-multivariate-data-analyses-and-elegant-visualization
- Source: http://machinelearningmastery.com/
- Sources: Decision Tree - https://en.wikipedia.org/wiki/Decision_tree_learning Instance-based - https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm Neural Networks - https://en.wikipedia.org/wiki/Artificial_neural_network Ensembles - https://www.analyticsvidhya.com/blog/2015/09/questions-ensemble-modeling/
- Sources: Greedy Search - https://en.wikipedia.org/wiki/Greedy_algorithm Gradient Descent - ?? Linear Programming - http://courses.wccnet.edu/~palay/math181/linearprogramming.htm
- Source: https://onlinecourses.science.psu.edu/stat857/node/160