1. Chapter 8: Extensions and Applications

2. Learning from Massive Datasets
Can it be held in main memory?---Naïve Byaes Method
Some learning schemes are incremental; some are not.
What about time it takes to model?—should be linear or near linear
What to do when data set is too large?
Use a small subset of data for training---law of diminishing returns
Some schemes do better with more data; but there is also a danger of overfitting
Parallelization is another way---develop parallelized versions of learning schemes

3. Incorporating Domain Knowledge :Metadata---data about data---semantic, causal, and functional
Text and web mining:
Adversarial situations: Junk filtering, for example