The document discusses machine learning and provides examples of its applications. It describes how the Netflix Prize offered $1 million to improve Netflix's recommendation system accuracy by 10%. It then defines machine learning as algorithms that allow computers to learn from experience with data to make predictions. Various machine learning algorithms are covered, including decision trees, naive Bayes, neural networks, k-nearest neighbors, and support vector machines. Applications discussed include spam filtering, document classification, recommendation systems, natural language processing, computer vision, bioinformatics, and finance.

1. Machine Learning Dell Zhang Birkbeck, University of London

3. Netflix Prize $1,000,000 to the first person to improve the accuracy of its recommendation system by 10 percent . $50,000 to the current leader each year for as long as the contest runs.

4. Concepts Learning = improving performance at some task through experience Prof. Tom Mitchell Human / Machine

5. Concepts Machine Learning (ML) is a subfield of artificial intelligence concerned with algorithms that allow computers to learn . What this means, in most cases, is that algorithm is given a set of data and infers information about the properties of data – and that information allows it to make predictions about other data that might see in the future.

6. Concepts This is possible because almost all non - random data contains patterns , and these patterns allow the machine to generalize . In order to generalize, it trains a model with what it determines are the important aspects of the data. Many machine learning algorithms rely heavily on mathematics (in particular statistics ) .

7. Concepts Closely Related Areas Data Mining or KDD Pattern Recognition

8. Problems Under-Generalizing Over-Generalizing Spam Filtering e.g., “Online Pharmacy”

10. Applications Document Classification Gmail’s Spam Filter PopFile Yahoo! MindSet Document Clustering Clusty Google News

11. Applications Recommendation Systems Amazon Netflix Pandora Last.fm Online Advertising MS adCenter Labs Research Demos

12. Applications Natural Language Processing Text Analysis Speech Recognition

13. Applications Machine Vision Handwritten Digit Recognition Face Recognition Vehicle Identification Intruder Detection National Security Anti-Terrorism

14. Applications Bioinformatics DNA Sequences RNA Expressions Protein Structures Finance Fraud Detection Stock Market Analysis

15. Algorithms Supervised Learning Decision Tree Naïve Bayes Neural Network k Nearest Neighbors Support Vector Machine Unsupervised Learning Semi-Supervised Learning

16. Decision Tree

17. Decision Tree

18. Decision Tree

20. Naïve Bayes

21. Naïve Bayes

22. Neural Network

23. Neural Network

24. Neural Network

25. k Nearest Neighbors The price for a new camera (6.0 megapixels, 6x zoom) = ?

26. k Nearest Neighbors

27. Support Vector Machine

28. Support Vector Machine

29. Support Vector Machine

30. Support Vector Machine

31. Support Vector Machine LIBSVM

32. Conclusion Statistical Learning as the Ultimate Agile Development Tool Peter Norvig (Director of Research, Google)

33. References