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DutchMLSchool. Supervised vs Unsupervised Learning

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Supervised versus Unsupervised Learning Techniques - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.

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DutchMLSchool. Supervised vs Unsupervised Learning

  1. 1. 1st edition | July 8-11, 2019
  2. 2. BigML, Inc #DutchMLSchool 2 Supervised vs. Unsupervised Charles Parker VP, Machine Learning Algorithms
  3. 3. BigML, Inc #DutchMLSchool 3 In This Talk • A definition of supervised vs. unsupervised learning • Several examples of machine learning problems of each type • Ways to tell if your problem is a machine learning problem or not
  4. 4. BigML, Inc #DutchMLSchool 4 Supervised Learning • In supervised learning, you “know the right answer” in the training data • The system is fed the right answer with the training examples • At prediction time, the goal of the system is to predict the right answer given only the input features
  5. 5. BigML, Inc #DutchMLSchool 5 Unsupervised Learning • In unsupervised learning, there is no obvious “right answer” • The system is fed the training examples, without any answer key • The system fits some structure to the examples • The attributes of the structure give us some insight into the data
  6. 6. BigML, Inc #DutchMLSchool 6 Rows: What is the Prediction About? • Rows (or instances or data points) in the dataset represent some entity about which we want to make a prediction • Churn prediction: A given customer in a given month • Medical diagnosis: A patient at a particular point in time • Stock market price prediction: A day in a particular market • Credit card fraud detection: A single transaction
  7. 7. BigML, Inc #DutchMLSchool 7 Columns: What do we know? • The columns (or features or fields) contain some information about the “entity” represented by each row • Churn prediction: Minutes used, calls to support • Medical diagnosis: BMI, temperature, test results • Stock market price prediction: Previous open and close, trend • Credit card fraud detection: Other recent transactions, geolocation
  8. 8. BigML, Inc #DutchMLSchool 8 Supervised Learning
  9. 9. BigML, Inc #DutchMLSchool 9 Objective: What Do We Want To Predict? • One of the columns is special; it contains the objective value, the value that we want to predict • Medical diagnosis: Positive or negative for a disease • Churn prediction: Customer did or did not churn • Credit card fraud detection: Transaction is or is not fraudulent • Market closing price forecast: The closing price on the given day
  10. 10. BigML, Inc #DutchMLSchool 10 When Is This a Good Idea? • Remember: Machine learning replaces expert and programmer with data and algorithm • So, to have a successful application: • The expert and / or the programmer must be unable to hold up their end of the bargain • We must have adequate data and a learning algorithm that gives sufficient performance
  11. 11. BigML, Inc #DutchMLSchool 11 It’s a Performance Critical Optimization • Wind farm optimization • Churn prediction • Fraud Detection • Market trading
  12. 12. BigML, Inc #DutchMLSchool 12 People Can’t Tell You How They Do It • Text recognition • Speech recognition • Music genre classification • Face recognition • Natural language understanding for market monitoring
  13. 13. BigML, Inc #DutchMLSchool 13 Experts Are Rare, Slow, or Expensive • Medical Diagnosis • Autonomous helicopter piloting • Game playing at extremely high levels • Bug identification
  14. 14. BigML, Inc #DutchMLSchool 14 Everyone Needs Their Own Algorithm • User-interface customization • E-mail / Document Grouping • Location prediction • Activity prediction via mobile
  15. 15. BigML, Inc #DutchMLSchool 15 When is this a Bad Idea? • Human centered workflows are perfectly fine • Printer defect detection • E-mail classification • Data is difficult to acquire or expensive to label • Medical data • Image labeling • Rare events (like major stock market discontinuities) • A program is easily written by hand or already exists • Credit card charge type • Whatever is already there!
  16. 16. BigML, Inc #DutchMLSchool 16 Unsupervised Learning
  17. 17. BigML, Inc #DutchMLSchool 17 Wait, No Objective? • With unsupervised learning, we don’t have an objective • But really, objectives are sort of artificial anyway • “Fit the parameters of a structure so that some quantity is optimized when the structure is applied to the training data that you have” • The structure and the quantity can really be anything
  18. 18. BigML, Inc #DutchMLSchool 18 Example: Topic Models • Suppose you have a bunch of text documents, and you want to know what’s in them • One way might be to come up with some topics; or groups of words that often occur together in the same document • So! A structure (the groupings of words) and a quantity to optimize (the percentage of the time that words in the same group occur together in the same document) • It’s only good if it’s useful!
  19. 19. BigML, Inc #DutchMLSchool 19 Types • Clustering (Find geometrically coherent groups in the dataset) • Association Rules (Find if-then rules that often hold true) • Topic Modeling (Find groups of words that occur together) • Anomaly Detection (Find unusual rows in the data) • Principle Components Analysis (Find the “true” number of features in the data)
  20. 20. BigML, Inc #DutchMLSchool 20 Data Exploration • Topic modeling for legal document discovery • Clustering for disease modality understanding • Anomaly detection for data debugging • Association Rules for correlation awareness
  21. 21. BigML, Inc #DutchMLSchool 21 Label Generation • Clustering for customer segmentation • Topic modeling for document organization • Anomaly detection for mislabeled data
  22. 22. BigML, Inc #DutchMLSchool 22 Feature Engineering • Topic modeling for document classification • Anomaly score for fraud detection • Clustering for customer churn prediction • PCA for RNA Microarray Data (or anything dense)
  23. 23. BigML, Inc #DutchMLSchool 23 Something To Show Your Boss • A particularly valuable customer segment • A rule that correlates the purchase of two unexpected things • An anomalous transaction that could use more scrutiny • It doesn’t have to be “novel” to be impressive
  24. 24. BigML, Inc #DutchMLSchool 24 So How Do You Tune Them? • A typical next question is, “How can I choose the parameters to get the best structure?” • One way is to optimize something about the structure that you want more of • Unfortunately, this can be time-consuming and often doesn’t produce what you want
  25. 25. BigML, Inc #DutchMLSchool 25 A Better Solution: Really Evaluate • Usually, a better solution comes from actually putting the model in a production-like situation (or simulating that) • Is someone going to be looking at the outputs? Do an AB-test with them. • Is another process downstream working with the outputs? Have it work with them. • Anywhere when you evaluate “honestly”, always do that instead of pulling some metric off of the shelf
  26. 26. Co-organized by: Sponsor: Business Partners:

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