ML: Review
- in Witten: Chapter 6 instance methods, clustering, EM, Bayesian Networks) 6.5, 6.7, 6.8
- Exam on Thursday: There will be a lecture/workshop after the exam, there will be one takehome problem due in Week 10.
Review for the Exam
- Bayes Rule, Naïve Bayes estimates
- Concepts, instances, attributes
- kmeans clustering (EM is optional)
- Decision trees
- Information gain
- decision rules
- coverage and accuracy
- regression and linear models
- Perceptron and neural networks
- ROC, you don’t need to memorize the formula, but know what it is
- type I and type II errors
Bayesian Networks
- What makes Naïve Base naïve