Topics
00 - Intro to Class
01 - Intro to Predictive Analytics
Readings
- Provost & Fawcett -- Chapter 3 -- up to "Supervised Segmentation"
02 - Supervised Segmentation
Readings
- Provost & Fawcett -- Chapter 3 -- from "Supervised Segmentation" onwards
03 - Discriminant Functions
Readings
- Provost & Fawcett -- Chapter 4
04 - Avoiding Overfitting (model performance)
Readings
- Provost & Fawcett -- Chapter 5
05 - Decision Analytics Thinking, Model Evaluation, and Expected Value Framework
Readings
- Provost & Fawcett -- Chapter 7
- Provost & Fawcett -- Chapter 11
06 - Visualizing Model Performance
Readings
- Provost & Fawcett -- Chapter 8
08 - Evidence Combination (Naive Bayes)
09 - Similarity and Nearest Neighbors
Readings
- Provost & Fawcett -- Chapter 6
10 - Unsupervised Data Mining and Clustering
Readings
- Provost & Fawcett -- Chapter 12
11 - Text Mining
Readings
- Provost & Fawcett -- Chapter 10
Labs
- Lab: Avoiding Overfitting
- Lab: Discriminant Functions
- Lab: Model Performance and Expected Value Framework
- Lab: Evidence Combination
- Lab: Supervised segmentation
- Lab: Text Mining