TOPICS for take-home exam of Oct 16

• Intro: What is Machine Learning (Chap 1)
• Supervised Learning: Some basic concepts (Chap 2)
• Bayes Decision Theory: Conditional Probability (Chap 3)
  . . .  Discriminant Functions, Normal Dist. (Chap 3)
• Estimating Gaussian Probability Densities, (Chap 4)
• Multivariate Methods: estimation and classification (Chap 5)
• Dimensionality Reduction: Principal Component Analysis (PCA) (Chap 6)
• Unsupervised Clustering: K-means (just the simple algorithm), Scatter matrix and Total Scatter (Chap 7)

TOPICS not included

• Fisher linear discriminant analysis (LDA)
• General probability density estimation
• Expectation-Maximization (EM) and generalizations of simple K-means.