Machine Learning
Definition
Machine learning (ML) is a subfield of artificial intelligence in which systems learn to perform tasks by finding patterns in data, rather than following hand-coded rules. A model is trained on examples and generalises to unseen inputs.
Core Ideas
Learning Paradigms
| Paradigm | Description | Examples |
|---|---|---|
| Supervised | Learn a mapping from inputs to labelled outputs | Regression, classification |
| Unsupervised | Find structure in unlabelled data | Clustering, dimensionality reduction |
| Reinforcement | Learn actions that maximise a reward signal | Game-playing agents, robotics |
| Self-supervised | Generate labels from the data itself | LLM pre-training, masked autoencoders |
Key Algorithms
Classification
- Logistic regression, support vector machines (SVM)
- Decision trees, random forests, gradient boosting (XGBoost, LightGBM)
- k-nearest neighbours
Regression
- Linear/polynomial regression, ridge, lasso
- Neural network regressors
Clustering
- k-means, DBSCAN, hierarchical clustering
Dimensionality Reduction
- PCA (linear), t-SNE, UMAP (non-linear)
Model Evaluation
- Classification: accuracy, precision, recall, F1, ROC/AUC
- Regression: MAE, MSE, RMSE, R²
- Validation: train/val/test split, k-fold cross-validation
- Overfitting signals: high training accuracy, low validation accuracy
Neural Networks
- Feedforward layers, activation functions (ReLU, sigmoid, tanh)
- Backpropagation and gradient descent
- Regularisation: dropout, batch normalisation, L2 weight decay
- Architectures: CNN (image), RNN/LSTM (sequence), Transformer (attention)
Relationships
- Retrieval-Augmented Generation (RAG) — ML-powered knowledge retrieval
- AI & Machine Learning — broader topic overview
- Trading & Finance — ML applied to price prediction and signals
- Cloud & AWS Infrastructure — deployment of ML models (SageMaker, EKS)