Final Project: Mathematics Behind a Machine Learning Model
Objective
Students will select one machine learning model and explain the mathematics that makes it work.
Project Components
- Description of the problem
- Mathematical ideas involved
- Visual explanation (plots or diagrams)
- Python implementation
- Reflection on assumptions and limitations
Example Models
- Linear Regression
- Logistic Regression
- k-NN
- Naive Bayes
- PCA
- Simple Neural Network
Evaluation Criteria
| Criterion | Weight |
| Mathematical Understanding | 40% |
| Clarity of Explanation | 25% |
| Python Implementation | 20% |
| Reflection & Insight | 15% |
