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 boosting and how it addresses hard to classify examples in the training data.
- Define and describe a random forest and list how they can be used to improve generalization error.
Resources
- Lecture Slides
Deliverables
| Topic | Description |
| Bias Variance Lab | Complete the in-class assignment and submit to Canvas |
| Random Forest lab | Complete the in-class assignment and submit to Canvas |