1. Machine Learning Lecture for Methodological Foundations of Biomedical Informatics Fall 2015 (BMSC-GA 4449) Sisi Ma NYU Langone Medical Center CHIBI

2. What type of problems can machine learning solve? Re Real Estate Artificial Intelligence Retail Sales Conservation Climate Current Active Projects on Kaggle as of Oct, 26 th,2015Kaggle

3. What type of problems can machine learning solve? Predominantly: Classification

4. How to classify? Main Ways to Classify: -Unsupervised -Supervised

5. Unsupervised Learning Group similar items together Comics credit: http://nlp.cs.berkeley.edu/comics.shtml

6. Unsupervised Learning Since the definition of similarity is arbitrary, one can get different labeling solutions.

7. Unsupervised Learning The solution depend on both: (1) what variables were used to construct the similarity metric (2) how the similarity metric were constructed.

8. Unsupervised Learning The solution depend on both: (1) what variables were used to construct the similarity metric (2) how the similarity metric were constructed.

9. Unsupervised Learning The solution depend on both: (1) what variables were used to construct the similarity metric (2) how the similarity metric were constructed. Lowe, 2012

10. Unsupervised Learning The solution depend on both: (1) what variables were used to construct the similarity metric (2) how the similarity metric were constructed. Image Credit: https://en.wikipedia.org/wiki/Metric_(mathematics)

11. Unsupervised Learning How do we know the solution is good? It corresponds to something we care about.

12. Unsupervised Learning

13. Supervised Learning

14. Overfitting Duda, 2ed

15. Supervised Learning Overfitting Image Credit: https://commons.wikimedia.org/wiki/File:Overfitting.svg

16. Supervised Learning How do I know if I am overfitting? Validation Data

17. Supervised Learning How do I know if I am overfitting? Duda, 2ed

18. Supervised Learning Support Vector Machine 18 Key Characteristics of SVM Maximum gap to prevent overfitting QP problems can be solved with standard methods. Soft margins to tolerate noise Kernel trick for linearly non-separable data Statnikov et al., 2011 Most modern algorithms have built in mechanism to minimize overfitting.

19. Predictive Modeling: A Simplified General Framework 19 Validation Data

20. Predictive Modeling: Cross Validation for error estimation and model selection 20 Ma et al., 2015 (in preparation)

21. Machine Learning vs Statistics Robert Tibshiriani

22. Machine Learning vs Statistics Robert Tibshiriani

23. Machine Learning vs Statistics Machine LearningStatistics One major difference between machine learning and statistics : How is the model evaluated?

24. Machine Learning vs Statistics What is a good model? According to most statistician, in practice especially Most commonly evaluated by R-squared Breiman, 2001

25. Machine Learning vs Statistics Validation Data What is a good model? According to machine learning researcher.

26. The Future

28. What’s the job?

33. Homework Research bias-variance decomposition and answer the following question from ” An Introduction to Statistical Learning”.

34. Resources