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)

• Part 1: Review of Probability Basics (1:00): http://youtu.be/dr_TPEsrD8s
• Part 2: Probabilistic Classifiers (1:20): http://youtu.be/MHcHUGPKCIc

Lecture Slides (PDF): 
http://datamining.it.nuigalway.ie/documents/CT475_07_ProbabilisticML.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.