Introduction to Machine Learning
- 1. Introduction to Machine Learning Presented By – Prof. Snehal
- 2. Why “Learn”? • Machine learning is programming computers to optimize a performance criterion using example data or past experience. • There is no need to “learn” to calculate payroll • Learning is used when: • Human expertise does not exist (navigating on Mars), • Humans are unable to explain their expertise (speech recognition) • Solution changes in time (routing on a computer network) • Solution needs to be adapted to particular cases (user biometrics) 2
- 3. What We Talk About When We Talk About“Learning” • Learning general models from a data of particular examples • Data is cheap and abundant (data warehouses, data marts); knowledge is expensive and scarce. • Example in retail: Customer transactions to consumer behavior: People who bought “Da Vinci Code” also bought “The Five People You Meet in Heaven” (www.amazon.com) • Build a model that is a good and useful approximation to the data. 3
- 4. Data Mining • Retail: Market basket analysis, Customer relationship management (CRM) • Finance: Credit scoring, fraud detection • Manufacturing: Optimization, troubleshooting • Medicine: Medical diagnosis • Telecommunications: Quality of service optimization • Bioinformatics: Motifs, alignment • Web mining: Search engines 4 Applications:
- 5. What is Machine Learning? • Machine Learning • Study of algorithms that • improve their performance • at some task • with experience • Optimize a performance criterion using example data or past experience. • Role of Statistics: Inference from a sample • Role of Computer science: Efficient algorithms to • Solve the optimization problem • Representing and evaluating the model for inference 5
- 6. Growth of Machine Learning • Machine learning is preferred approach to • Speech recognition, Natural language processing • Computer vision • Medical outcomes analysis • Robot control • Computational biology • This trend is accelerating • Improved machine learning algorithms • Improved data capture, networking, faster computers • Software too complex to write by hand • New sensors / IO devices • Demand for self-customization to user, environment • It turns out to be difficult to extract knowledge from human experts failure of expert systems in the 1980’s. 6
- 7. Applications • Association Analysis • Supervised Learning • Classification • Regression/Prediction • Unsupervised Learning • Reinforcement Learning 7
- 8. Learning Associations • Basket analysis: P (Y | X ) probability that somebody who buys X also buys Y where X and Y are products/services. Example: P ( chips | beer ) = 0.7 Market-Basket transactions TID Items 1 Bread, Milk 2 Bread, Diaper, Beer, Eggs 3 Milk, Diaper, Beer, Coke 4 Bread, Milk, Diaper, Beer 5 Bread, Milk, Diaper, Coke
- 9. Classification: Applications • Aka Pattern recognition • Face recognition: Pose, lighting, occlusion (glasses, beard), make-up, hair style • Character recognition: Different handwriting styles. • Speech recognition: Temporal dependency. • Use of a dictionary or the syntax of the language. • Sensor fusion: Combine multiple modalities; eg, visual (lip image) and acoustic for speech • Medical diagnosis: From symptoms to illnesses • Web Advertising: Predict if a user clicks on an ad on the Internet.
- 10. Face Recognition 10 Training examples of a person Test images AT&T Laboratories, Cambridge UK http://www.uk.research.att.com/facedatabase.html
- 11. Supervised Learning: Uses • Prediction of future cases: Use the rule to predict the output for future inputs • Example: decision trees tools that create rules • Knowledge extraction: The rule is easy to understand • Compression: The rule is simpler than the data it explains • Outlier detection: Exceptions that are not covered by the rule, e.g., fraud 11
- 12. Unsupervised Learning • Learning “what normally happens” • No output • Clustering: Grouping similar instances • Other applications: Summarization, Association Analysis • Example applications • Customer segmentation in CRM • Image compression: Color quantization • Bioinformatics: Learning motifs 12
- 13. Reinforcement Learning • Topics: • Policies: what actions should an agent take in a particular situation • Utility estimation: how good is a state (used by policy) • No supervised output but delayed reward • Credit assignment problem (what was responsible for the outcome) • Applications: • Game playing • Robot in a maze • Multiple agents, partial observability, ...