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Oct2019 - What is machine learning?
- 2. Contents • Attempt at definition • Applications • Techniques • Considerations • Sources • Software • ML in R and Python 14-10-2019 page 2
- 3. 14-10-2019 page 3 Definition Applications Techniques Considerations Sources Software ML in R & Python
- 4. Attempt at definition Machine Learning • “… gives computers the ability to learn without being explicitly programmed” (Arthur Samuel, 1959) • “… is the systematic study of algorithms and systems that improve their knowledge or performance with experience” (Peter Flach, 2012) • “… concerns systems that automatically learn programs from data” (Pedro Domingos, 2012) 14-10-2019 page 4 Definition Applications Techniques Considerations Sources Software ML in R & Python
- 5. Related to ML • Artificial Intelligence • Knowledge discovery • (Predictive) Analytics • Statistics / Statistical Learning • Optimization • Evolutionary algorithms • Deep Learning • Data Mining • Pattern recognition • Data Science • Informatics, computer/computational science • Econometrics • Related buzzwords: Big Data, Internet of Things 14-10-2019 page 5 Definition Applications Techniques Considerations Sources Software ML in R & Python
- 6. Related fields Artificial Intelligence Data Science Statistics Informatics Econometrics Optimization 14-10-2019 page 6 Definition Applications Techniques Considerations Sources Software ML in R & Python
- 7. Terminology Statistics/Econometrics Machine Learning Independent variables, predictors Features, inputs Dependent variable Output, response Estimation, fitting Training, learning Dummy coding One-hot encoding Transformation of variables Feature engineering Parameters Weights Regression/classification Supervised learning Goal is to understand (model) Goal is to predict 14-10-2019 page 7 Definition Applications Techniques Considerations Sources Software ML in R & Python
- 8. ML Applications • Handwriting recognition • Facial/image recognition • Speech recognition • Spam filters • Text Mining • DNA sequence classification • Search engines • Stock market analysis • Game playing • Medical diagnostics • Fraud detection • Passenger screening • Crime prediction • Satellite image classification • Robotics • Automatic flight pilots • Self-driving cars • … 14-10-2019 page 8 Definition Applications Techniques Considerations Sources Software ML in R & Python
- 9. 14-10-2019 page 9 Definition Applications Techniques Considerations Sources Software ML in R & Python
- 10. Recommendations 14-10-2019 page 10 Definition Applications Techniques Considerations Sources Software ML in R & Python
- 11. “Object” recognition in computer vision 14-10-2019 page 11 Definition Applications Techniques Considerations Sources Software ML in R & Python
- 12. ML Techniques Three groups: • Supervised learning (classification, regression) • Unsupervised learning (PCA, clustering, …) • Reinforcement learning (agent-based) • (Transfer learning) • … What do you need for a self-driving car? 14-10-2019 page 12 Definition Applications Techniques Considerations Sources Software ML in R & Python
- 13. Supervised techniques Specific for Classification • Decision trees • Bagged trees • Boosted trees • Random Forests • Neural networks • Support Vector Machines • Genetic programming • Bayesian Networks • MARS • Lasso • Logistic regression • Naive Bayes • kNN • Ensemble models • … 14-10-2019 page 13 Definition Applications Techniques Considerations Sources Software ML in R & Python
- 14. Techniques 14-10-2019 page 14 Definition Applications Techniques Considerations Sources Software ML in R & Python
- 15. Techniques 14-10-2019 page 15 Definition Applications Techniques Considerations Sources Software ML in R & Python
- 16. Example Unsupervised: Anomaly detection boundary case outlier extreme case Robust regression: MVE estimation 14-10-2019 page 16 Definition Applications Techniques Considerations Sources Software ML in R & Python
- 17. Optimisation techniques • Linear programming • (Mixed) Integer Programming • Non-linear programming • Modern optimisation techniques: 14-10-2019 page 17 Definition Applications Techniques Considerations Sources Software ML in R & Python
- 18. Considerations • Similarity between ML and statistical methods is often big: representation – evaluation – optimisation • Personal note: if it works well (prediction!), use it! (but explainability may also matter) • Data preparation (incl. feature engineering) often is 80% of the work • Bias-Variance dilemma remains (overfitting) • Perform fair comparison using ROC-curves on independent testset • “No free lunch”: no single technique is always best => Use expert knowledge and choose representation fitting the problem (data alone is not enough) • Curse of dimensionality: input space grows exponentially with k, the number of observations (generally) does not • Consider making multiple models and combining (ensembles) 14-10-2019 page 18 Definition Applications Techniques Considerations Sources Software ML in R & Python
- 19. Sources • Literature (use Google Scholar and arXiv) • Data (Kaggle, UCI, Quandl, governments, APIs) • Competitions (Kaggle, Topcoder, HackerRank, CrowdAnalytix) • Courses (Coursera, Udacity, Udemy, DataCamp) • Academic education (A’dam School of Data Science, Eindhoven, Delft, Tilburg) • Fora (Kaggle, Stackoverflow, Quora) • Other websites (Analytics Vidhya, Data Science Central, DeepMind, DutchDigitalDelta-Commit2data) • 14-10-2019 page 19 s (e.g. ADS and AMDS) Definition Applications Techniques Considerations Sources Software ML in R & Python
- 20. Literature • Articles – A Few Useful Things to Know About Machine Learning (Pedro Domingos, CACM Oct 2012) – Statistical Modeling: The Two Cultures (Leo Breiman, Statistical Science 2001) • Books – The Elements of Statistical Learning (Hastie/Tibshirani/Friedman; Springer 2008) – Applied Predictive Modeling (Kuhn/Johnson; Springer 2013) – Machine Learning (Flach; Cambridge Univ. Press 2012) – Reinforcement Learning: An Introduction (Sutton/Barto; MIT Press 2012) – Artificial Intelligence: A Modern Approach 3rd ed. (Russell/Norvig; Prentice Hall 2016) – Modern Optimization with R (Cortez; Springer 2014) 14-10-2019 page 20 Definition Applications Techniques Considerations Sources Software ML in R & Python
- 21. Software • General purpose programming languages – Python – R – SAS – Matlab • ML environments/libraries – MS Azure – Google Tensorflow (for R) – AWS: Amazon Web Services 14-10-2019 page 21 Definition Applications Techniques Considerations Sources Software ML in R & Python
- 22. Machine Learning in R • CRAN Task View: Machine Learning & Statistical Learning • Caret package – Vignette – Many model types – Training and prediction – Variable importance – Parameter tuning – Cross-Validation, ROC curves, plots – etc. • Tensorflow interface (via Python) 14-10-2019 page 22 Definition Applications Techniques Considerations Sources Software ML in R & Python
- 23. Machine Learning in Python 14-10-2019 page 23 Definition Applications Techniques Considerations Sources Software ML in R & Python • scikit-learn • pytorch (for deep learning) • (auto-ml)
- 24. Reinforcement Learning • MDP: Markov Decision Process • Environment (S,A,P,R) entirely or partly known • Packages in R – MDPtoolbox – ReinforcementLearning • Code in Python – Lots on github, e.g. DeepMind TRFL • Self coding 14-10-2019 page 24
- 25. Hype or Hope? 14-10-2019 page 25
- 27. Hype or Hope? 14-10-2019 page 27
- 28. Hype or Hope? 14-10-2019 page 28