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Supervised Unsupervised and Reinforcement Learning
The document provides an overview of machine learning, detailing its types: supervised, unsupervised, and reinforcement learning. It covers the fundamental concepts, applications, and specific methodologies associated with each type, such as function approximation and clustering. The conclusion emphasizes the learning mechanisms in each category, particularly the role of data and feedback in model building.
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Supervised Unsupervised and Reinforcement Learning
- 1. Machine Learning Aakash Chotrani(MSCS) Supervised, Unsupervised and Reinforcement Learning
- 2. Machine Learning Supervised, Unsupervisedand Reinforcement Learning A. What is Machine Learning? B. Applications of Machine Learning C. Supervised Learning D. Unsupervised Learning E. Reinforcement Learning F. Conclusion
- 3. Data is theKing
- 4. What is MachineLearning? GOAL = Building models for predicting value/class of unseen data
- 5. Solving a generalproblem Algorithms are the Central part
- 6. Solving a Machinelearning problem Algorithms + Data = Central part
- 7. Machine Learning Supervised, Unsupervisedand Reinforcement Learning A. What is Machine Learning? B. Applications of Machine Learning C. Supervised Learning D. Unsupervised Learning E. Reinforcement Learning F. Conclusion
- 8.
- 9. Machine Learning Supervised, Unsupervisedand Reinforcement Learning A. What is Machine Learning? B. Applications of Machine Learning C. Supervised Learning D. Unsupervised Learning E. Reinforcement Learning F. Conclusion
- 10. Supervised Learning Learning fromthe association between Input and Output Input: Output: 1 3 4 7 1 9 16 49 10 ? F(x) = x2 (Function Approximation)
- 11. Another Practical Exampleof Supervised Learning $ Years of experience
- 12. Data Preprocessing Training set Testset Dataset
- 13. Plotting training seton graph
- 14. Equation for straightline(Function Approximation) y = b0 + b1*x1 Dependent Variable (salary) Independent Variable (Years of experience)
- 15.
- 16. Different Regression models PolynomialRegression Support vector regression Decision Tree Regression Random Forest Regression
- 17. Some Applications ofSupervised Learning
- 18. Machine Learning Supervised, Unsupervisedand Reinforcement Learning A. What is Machine Learning? B. Applications of Machine Learning C. Supervised Learning D. Unsupervised Learning E. Reinforcement Learning F. Conclusion
- 19. Unsupervised Learning Find Interestingthings about the data(clusters)
- 20. Unsupervised Learning We derivethe structure from input by just looking at relation between Input themselves Finding structure within the data without labels Low earning group High earning group
- 21. What we tryto Achieve We try to organize something Example: We have 20 million data items and we want to group them We make up some sort to criteria to find a cluster that scores well
- 22.
- 23. Machine Learning Supervised, Unsupervisedand Reinforcement Learning A. What is Machine Learning? B. Applications of Machine Learning C. Supervised Learning D. Unsupervised Learning E. Reinforcement Learning F. Conclusion
- 24. Reinforcement Learning Learning fromdelayed reward Agent Environment State Action Reward
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- 27. Reinforcement Learning • Feedbackafter several steps • We try to find the behavior which scores well • Computation happens within the agent. • No idea about the environment beforehand • Learns about the environment through interaction with the environment
- 28. Conclusion Supervised Learning • Learningthrough delayed feedback by interacting with environment Reinforcement Learning Unsupervised Learning • We are trying to find association between input values and grouping them • We are performing function approximation based on input and output values
- 29.