Pattern Processing - Module 3F3

Lecture Notes:

• Lecture 1: Introduction
  
• Lecture 2: Regression
  
• Lecture 3: Classification
  
• Lecture 4: Clustering
  
• Lecture 5: Dimensionality Reduction
  

• Examples Paper

• Maximum Likelihood PolynomialRegression: genreg.m
• Bayesian Polynomial Regression: polybayes.m
• Online Linear Logistic Classification: lrdemo.m
• Online Non-linear Logistic Classification: nlrdemo.m
• K-means Clustering: kmeansdemo.m
• Iris Data: iris.data (needed for K-means demo)

Simon Godsill's lectures (2011) on this module are at this website

If you are interested in this topic, you may want to look at:

• '4F13: Machine Learning' module website.
• '4F10: Statistical Pattern Processing' module website.