L1. State of the Art in Machine Learning
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The document provides an overview of machine learning (ML), defining it as a field enabling computers to learn from data without explicit programming. It discusses the types of learning problems such as underfitting and overfitting, various ML models, and the significance of data management, including handling missing values. Additionally, it traces the evolution of ML techniques from the 1950s to present, highlighting the reasons for its current prominence and outlining the stages involved in developing an ML application.
L1. State of the Art in Machine Learning
- 1. State of the Art in Machine Learning Poul Petersen BigML
- 2. 2 What is ML? “a field of study that gives computers the ability to learn without being explicitly programmed” Professor Arthur Samuel, 1959 •What “ability to learn” do computers have? •What does “explicitly programmed” mean?
- 3. Square Feet Price Sacramento Real Estate Prices by sq_ft 3 Ability to Learn
- 4. 4 Ability to Learn • Provide computer with examples of the relationship between square footage X and price Y. • The computer learns the equation of a line F() which fits the examples. • The computer can now predict the price of any home knowing only the square footage: F( x ) = y • This model is known as Linear Regression. There are other types of models. • You may have noticed there was some points that did not fit. This is important!
- 5. 5 Learning Problems (fit) Under-fitting Over-fitting • Model does not fit well enough • Does not capture the underlying trend of the data • Change algorithm or features • Model fits too well does not “generalize” • Captures the noise or outliers of the data • Change algorithm or filter outliers
- 6. 6 Learning Problems (missing) • Missing values at training/prediction time • Some algorithms can handle missing values, some no • Missing data is sometimes important • Replace missing values • Predict missing values
- 7. 7 Learning Problems (missing) Missing@ Decision Trees KNN Logistic Regression Naive Bayes Neural Networks Training Yes No No Yes Yes* Prediction Yes No No Yes No
- 8. 8 Not Explicitly Programmed Control System “Customers go on vacation… We need a program that predicts if the light will come on when the control system flips the switch.”
- 9. 9 Not Explicitly Programmed @iLoveRuby Switch Light? on TRUE off FALSE • @iLoveRuby reasoned the rules by experience • Then programmed the rules explicitly
- 10. 10 Not Explicitly Programmed Switch Light? on TRUE off FALSE • The ML System reasoned the rules from the data and created a Model • Functionally the Model is the same as @iLoveRuby’s explicit model @iLoveML question answer ML System Model
- 11. 11 Not Explicitly Programmed • how long has the bulb has been in service • reliability of brand • rated power: higher power shorter life • duty cycle • room conditions: temperature, humidity Goal is to predict if the bulb will come on but the switch is not the important variable: Even worse: None of these conditions is absolute.
- 12. 12 Not Explicitly Programmed brand power age duty temp humidity FAIL? koala 45 338 1 16 0.03 FALSE otter 15 140 1 27 0.27 FALSE koala 15 315 1 19 0.37 TRUE otter 45 338 1 29 0.27 TRUE koala 45 211 1 23 0.85 TRUE otter 15 328 1 17 0.56 FALSE koala 15 318 2 22 0.45 TRUE koala 15 273 1 27 0.18 FALSE koala 45 102 1 21 0.48 FALSE koala 15 110 2 15 0.99 TRUE otter 45 355 2 15 0.01 FALSE otter 15 69 1 24 0.70 FALSE koala 15 69 1 24 0.70 FALSE koala 15 337 2 27 0.83 TRUE
- 13. 13 Not Explicitly Programmed @iLoveRuby • Multi-dimensional data is much harder to find rules • Explicit program requires modification brand power age duty temp humidity FAIL? koala 45 338 1 16 0.03 FALSE otter 15 140 1 27 0.27 FALSE koala 15 315 1 19 0.37 TRUE otter 45 338 1 29 0.27 TRUE koala 45 211 1 23 0.85 TRUE otter 15 328 1 17 0.56 FALSE koala 15 318 2 22 0.45 TRUE koala 15 273 1 27 0.18 FALSE koala 45 102 1 21 0.48 FALSE koala 15 110 2 15 0.99 TRUE otter 45 355 2 15 0.01 FALSE otter 15 69 1 24 0.70 FALSE koala 15 69 1 24 0.70 FALSE koala 15 337 2 27 0.83 TRUE
- 14. @iLoveML ML System Model 14 Not Explicitly Programmed • ML System easily re-trains on new data question answer brand power age duty temp humidity FAIL? koala 45 338 1 16 0.03 FALSE otter 15 140 1 27 0.27 FALSE koala 15 315 1 19 0.37 TRUE otter 45 338 1 29 0.27 TRUE koala 45 211 1 23 0.85 TRUE otter 15 328 1 17 0.56 FALSE koala 15 318 2 22 0.45 TRUE koala 15 273 1 27 0.18 FALSE koala 45 102 1 21 0.48 FALSE koala 15 110 2 15 0.99 TRUE otter 45 355 2 15 0.01 FALSE otter 15 69 1 24 0.70 FALSE koala 15 69 1 24 0.70 FALSE koala 15 337 2 27 0.83 TRUE
- 15. @iLoveML ML System Model 15 Terminology input prediction brand power age duty temp humidity FAIL? koala 45 338 1 16 0.03 FALSE otter 15 140 1 27 0.27 FALSE koala 15 315 1 19 0.37 TRUE otter 45 338 1 29 0.27 TRUE koala 45 211 1 23 0.85 TRUE otter 15 328 1 17 0.56 FALSE koala 15 318 2 22 0.45 TRUE koala 15 273 1 27 0.18 FALSE koala 45 102 1 21 0.48 FALSE koala 15 110 2 15 0.99 TRUE otter 45 355 2 15 0.01 FALSE otter 15 69 1 24 0.70 FALSE koala 15 69 1 24 0.70 FALSE koala 15 337 2 27 0.83 TRUE datasource training “putting into production”
- 16. 16 Supervised Learning brand power age duty temp humidity FAIL? koala 45 338 1 16 0.03 FALSE otter 15 140 1 27 0.27 FALSE koala 15 315 1 19 0.37 TRUE otter 45 338 1 29 0.27 TRUE koala 45 211 1 23 0.85 TRUE otter 15 328 1 17 0.56 FALSE koala 15 318 2 22 0.45 TRUE features instances label Labeled Data
- 17. 17 Supervised Learning animal state … proximity action tiger hungry … close run elephant happy … far take picture Classification animal state … proximity min_kmh tiger hungry … close 70 hippo angry … far 10 Regression label animal state … proximity action1 action2 tiger hungry … close run look untasty elephant happy … far take picture call friends Multi-Label Classification
- 18. 18 Unsupervised Learning date customer account auth class zip amount Mon Bob 3421 pin clothes 46140 135 Tue Bob 3421 sign food 46140 401 Tue Alice 2456 pin food 12222 234 Wed Sally 6788 pin gas 26339 94 Wed Bob 3421 pin tech 21350 2459 Wed Bob 3421 pin gas 46140 83 The Sally 6788 sign food 26339 51 features instances Unlabeled Data
- 19. 19 Unsupervised Learning date customer account auth class zip amount Mon Bob 3421 pin clothes 46140 135 Tue Bob 3421 sign food 46140 401 Tue Alice 2456 pin food 12222 234 Wed Sally 6788 pin gas 26339 94 Wed Bob 3421 pin tech 21350 2459 Wed Bob 3421 pin gas 46140 83 The Sally 6788 sign food 26339 51 Clustering date customer account auth class zip amount Mon Bob 3421 pin clothes 46140 135 Tue Bob 3421 sign food 46140 401 Tue Alice 2456 pin food 12222 234 Wed Sally 6788 pin gas 26339 94 Wed Bob 3421 pin tech 21350 2459 Wed Bob 3421 pin gas 46140 83 The Sally 6788 sign food 26339 51 Anomaly Detection similar unusual
- 20. 20 Semi-Supervised Learning brand power age duty temp humidity FAIL? koala 45 338 1 16 0.03 FALSE otter 15 140 1 27 0.27 koala 15 315 1 19 0.37 otter 45 338 1 29 0.27 TRUE koala 45 211 1 23 0.85 TRUE otter 15 328 1 17 0.56 koala 15 318 2 22 0.45 TRUE label Labeled and Unlabeled Data }infer
- 21. 21 Data Types numeric 1 2 3 1, 2.0, 3, -5.4 categoricaltrue, yes, red, mammal categoricalcategorical A B C DATE-TIME2013-09-25 10:02 DATE-TIME YEAR MONTH DAY-OF-MONTH YYYY-MM-DD DAY-OF-WEEK HOUR MINUTE YYYY-MM-DD YYYY-MM-DD M-T-W-T-F-S-D HH:MM:SS HH:MM:SS 2013 September 25 Wednesday 10 02 text Be not afraid of greatness: some are born great, some achieve greatness, and some have greatness thrust upon 'em. text “great” “afraid” “born” “some” appears 2 times appears 1 time appears 1 time appears 2 times
- 22. 22 Text Analysis Be not afraid of greatness: some are born great, some achieve greatness, and some have greatness thrust upon 'em.
- 23. 22 Text Analysis Be not afraid of greatness: some are born great, some achieve greatness, and some have greatness thrust upon 'em. great: appears 4 times
- 24. 23 Text Analysis great afraid born achieve 4 1 1 1 … … … … Be not afraid of greatness: some are born great, some achieve greatness, and some have greatness thrust upon ‘em. Model The token “great” does not occur The token “afraid” occurs more than once
- 26. 25 Brief History of ML 1950 1960 1970 1980 1990 2000 2010 Perceptron Neural Networks Ensembles Support Vector Machines Boosting Interpretability Rosenblatt, 1957 Quinlan, 1979 (ID3), Minsky, 1969 Vapnik, 1963 Corina & Vapnik, 1995 Schapire, 1989 (Boosting) Schapire, 1995 (Adaboost) Breiman, 2001 (Random Forests) Breiman, 1994 (Bagging) Deep Learning Hinton, 2006Fukushima, 1989 (ANN) Breiman, 1984 (CART) 2020 + - Decision Trees
- 27. 26 Why ML Now? • Decreasing cost of data • Abundant computing power, especially cloud • Machine Learning APIs • Abundance of APIs + internet to combine easily
- 28. 27 Composability
- 29. 28 The Stages of a ML App State the problem Data Wrangling Feature Engineering Learning Deploying Predicting Measuring Impact