# Mathematics of machine learning

> Work through regression, gradients, classification and generalization using paper-based examples.

Prerequisites: Algebra, averages and basic derivatives.

Short self-study course; not an accredited qualification or a complete university syllabus.

## Lessons

- [Linear regression, residuals and squared loss](https://studybrix.com/en/courses/machine-learning-mathematics/ai-linear-regression/index.md): university; 8 questions.
- [Gradient descent and learning-rate choices](https://studybrix.com/en/courses/machine-learning-mathematics/ai-gradient/index.md): university; 8 questions.
- [Logistic scores, thresholds and confusion matrices](https://studybrix.com/en/courses/machine-learning-mathematics/ai-classification/index.md): university; 8 questions.
- [Overfitting, regularization and data distribution](https://studybrix.com/en/courses/machine-learning-mathematics/ai-generalization/index.md): university; 8 questions.