1. 1 1)Bayes’ Theorem 2)MAP, ML Hypothesis 3)Bayes optimal & Naïve Bayes classifiers IES 511 Machine Learning Dr. Türker İnce (Lecture notes by Prof. T. M. Mitchell, Machine Learning course at CMU)

2. 2 Bayesian Learning

3. 3 Bayesian Methods

4. 4 Bayes’ Theorem

5. 5 Choosing Hypotheses

6. 6 Example

7. 7 Basic Formulas for Probabilities

8. 8 Brute Force MAP Hypothesis Learner

9. 9 Bayes Theorem and Concept Learning

10. 10 Bayes Theorem and Concept Learning

11. 11 Evolution of Posterior Probabilities

12. 12 Equivalent MAP learner for Candidate- Elimination Algorithm

13. 13 Learning a real-valued function

14. 14 Maximum Likelihood and Least-Squared Error Hypotheses

15. 15 Maximum Likelihood and Least-Squared Error Hypotheses

16. 16 Learning to Predict Probabilities

17. 17 Minimum Description Length Principle

18. 18 Minimum Description Length Principle

19. 19 Most Probable Classification of New Instances

20. 20 Bayes Optimal Classifier

21. 21 Example

22. 22 Gibbs Classifier

23. 23 Naïve Bayes Classifier

24. 24 Naïve Bayes Classifier

25. 25 Naïve Bayes Algorithm

26. 26 PlayTennis Example

27. 27 Bayesian Belief Networks (Bayes Nets)

28. 28