Topics for Test 1.  

   from Chapter 2: Supervised Learning
       2.1: Learning a Class from Examples
       2.2: Vapnik-Chervonenkis Dimension
       2.5: Learning Multiple Classes
       2.6: Regression
       2.8: Dimensions of a Supervised Machine Learning Algorithm

   from Chapter 3: Bayesian Decision Theory
       3.1: Introduction
       3.2: Classification
       3.3: Losses and Risks
       3.4: Discriminant Functions
       Notes from web site

   from Chapter 4: Parametric Methods
       4.1: Introduction
       4.2: Maximum Likelihood Estimation
       4.4: The Bayes' Estimator
       4.5: Parametric Classification
       4.6: Regression

   from Chapter 5: Multivariate Methods
       5.1: Multivariate Data
       5.2: Parameter Estimation
       5.4: Multivariate Normal Distribution
       5.5: Multivariate Classification
       5.8: Multivariate Regression
       Notes from web site

   from Chapter 10: Linear Discrimination
       10.5: Parametric Discrimination Revisited
       10.6: Gradient Descent
       10.7: Logistic Discrimination [two-class only]