CT475: Machine Learning & Data Mining

Topic 7: Probabilistic Machine Learning

Dr Michael Madden, National University of Ireland Galway

This course is normally delivered in a classroom setting, but I am posting some recorded lectures online. 

Lecture Videos (see below for details of contents)

Lecture Slides (PDF): 

Calculations for Spam Filter Example (XLSX): http://datamining.it.nuigalway.ie/documents/NaiveBayesSpam.xlsx

Contents of Part 1: Review of Probability Basics

  • Introduction & Learning Objectives
  • Why Consider Uncertainty?
  • Summary of Techniques for Handling Uncertainty
  • Review of Probability
  • Probability Notation
  • Axioms of Probability
  • Unconditional and Conditional Probability
  • Joint Probability Distribution
  • Independence & Conditional Independence
  • Product Rule, Total Probability, & Bayes’ Rule

Contents of Part 2: Probabilistic Classifiers

  • Reasoning with Bayes’ Rule
  • Challenges in Estimating Probabilities
  • Bayes’ Rule: Example
  • Bayes’ Rule with Normalisation
  • Bayes’ Rule: Combining Evidence & Updating
  • Naïve Bayes Classifier
  • Example: Play Tennis
  • Example: Bayesian Spam Filter


 Small Tutorials

  1. Excel-Based Tutorial on Computing a Decision Matrix and Plotting a ROC Curve
  2. Excel-Based Tutorial on Paired T-Tests for Comparing Classifier Results.