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Machine Learning Presentation for Engineering
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- 2. Growth of MachineLearning ■ 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. Alpydin & Ch. Eick: ML Topic1 2
- 3. AI vs MLvs Deep Learning 3
- 4. Machine Learning vsDeep Learning 4
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- 6. 6 Applications ■ Association Analysis ■Supervised Learning ◻ Classification ◻ Regression/Prediction ■ Unsupervised Learning ■ Reinforcement Learning
- 7. Alpydin & Ch.Eick: ML Topic1 7
- 8. Alpydin & Ch.Eick: ML Topic1 8
- 9. Alpydin & Ch.Eick: ML Topic1 9
- 10. Alpydin & Ch.Eick: ML Topic1 10
- 11. 11 Classification ■ Example: Credit scoring ■Differentiating between low-risk and high-risk customers from their income and savings Discriminant: IF income > θ1 AND savings > θ2 THEN low-risk ELSE high-risk Model
- 12. 12 Classification: Applications ■ AkaPattern 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 Advertizing: Predict if a user clicks on an ad on the Internet.
- 13. 13 Face Recognition Training examplesof a person Test images AT&T Laboratories, Cambridge UK http://www.uk.research.att.com/facedatabase.html
- 14. 14 Prediction: Regression ■ Example:Price of a used car ■ x : car attributes y : price y = g (x | θ ) g ( ) model, θ parameters y = wx+w0
- 15. 15 Regression Applications ■ Navigatinga car: Angle of the steering wheel (CMU NavLab) ■ Kinematics of a robot arm α1= g1(x,y) α2= g2(x,y) α1 α2 (x,y)
- 16. 16 Supervised Learning: Uses ■Prediction of future cases: Use the rule to predict the output for future inputs ■ 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 Example: decision trees tools that create rules
- 17. 17 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
- 18. Learning Associations ■ Basketanalysis: P (Y | X ) probability that somebody who buys X also buys Y where X and Y are products/services. Example: P ( Bread | Milk ) = 0.6 Market-Basket transactions
- 19. 19 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, ...
- 20. Reinforcement learning ■ Reinforcementlearning is also based on feedback provided by the environment. However, in this case, the information is more qualitative and doesn't help the agent in determining a precise measure of its error. ■ this feedback is usually called reward (sometimes, a negative one is defined as a penalty) and it's useful to understand whether a certain action performed in a state is positive or not. 20
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