CS 445 Machine Learning
Fall 2020
Week 7 Study Guide
Learning Objectives
- Probability computations including:marginalization (summing out), conditional probabilities, chain rule, and bayes therom
- Define conditional independance and the how it simplifiers the number of probabilities needed compared to the full joint distribution
- Define an algorithm to perform Naïve Bayes and characterize the "naïve" assumption (is it correct? does it work?)
Resources
- Lecture Slides
Labs
Deliverables
| Topic | Description |
| Probabiilty Refresher Lab | Complete the in-class assignment and submit to Canvas |
| Naive Bayes lab | Complete the in-class assignment and submit to Canvas |