Week 8 - Naive Bayes

Learning Objectives

After today's class, you should be able to:

• Understand the probabilistic Naive Bayes algorithm for classification tasks, including how it models feature likelihoods using Bayes’ Theorem with a conditional independence assumption.
• Calculate probabilities, develop and implement Naive Bayes models in Python.

Class Agenda

• [30 min] Exam 1 Results
• Naive Bayes Slides
• [5 min] Break (Optional)
• KNN Demo
• [30 min] Python Q&A/Debugging Help

Task List

• Required Reading: Chapter 8 - The Naive Bayes Classifier
• Complete and Submit HW5_Naive_Bayes on Canvas.

Additional Resources

• Chapter 8 Code Examples