Machine Learning (ML)
This section contains notes on ML foundations, pipelines, learning paradigms, and model families.
1. Pipeline
The stages of a general ML pipeline, ranging from data preparation to model interpretation.
1.1. Pre-Processing
1.2. Model Selection
1.3. Model Evaluation
1.4. Model Explanation
2. Supervised Methods
Methods that learn mappings from inputs to outputs using labeled data.
2.1. Linear Models
2.2. Distance and Margin Models
2.3. Tree Models
2.4. Probabilistic Models
3. Unsupervised Methods
Methods for discovering patterns without explicit labels.