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Navigable Slide Index

1. Introduction
2. Agenda: linear models
3. Linear models
4. Classification by regression
5. Advanced linear models
6. Support vector machine
7. Multilayer perceptron
8. Trees for numeric prediction
9. Linear models: discussion
10. Agenda: instance-based learning and clustering
11. Instance-based learning (1)
12. Instance-based learning (2)
13. Clustering
14. Hierarchical clustering
15. Iterative: fixed num of clusters
16. Probabilistic clustering
17. Using the mixture model
18. Extending the mixture model
19. Bayesian clustering
20. Agenda: engineering the input and output
21. Engineering the input and output
22. Attribute selection
23. Data transformations
24. Principal component analysis
25. Combining multiple models
26. Bagging
27. Randomization
28. Boosting
29. Stacking
30. Using unlabeled data
31. Co-training
32. Talk summary
33. Data mining algorithms

Topics Covered

• Linear models
• Classification by regression
• Support vector machine
• Multilayer perceptron
• Trees for numeric prediction
• Instance based learning and clustering
• Hierarchical clustering
• Iterative: fixed num of clusters
• Probabilistic clustering
• Using and extending the mixture model
• Bayesian clustering
• Engineering the input and output
• Attribute selection
• Data transformations
• Principal component analysis
• Combining multiple models
• Bagging
• Randomization
• Boosting
• Stacking
• Using unlabeled data
• Co-training

Links

Series:

• Data Mining

Categories:

• Marketing & Sales
• Technology & Operations

Talk Citation

Publication History

• Published on June 17, 2008
• Archived on March 17, 2022