Computer Science and Engineering

Pattern recognition
  1. Feature Representation
  2. Mean and Covariance
  3. Linear Perceptron Learning
  4. Generation of Random Variables
  5. Bayesian Classification
  6. MLE: Learning the classifier from data
  7. Data Clustering: K-Means, MST-based