Machine Learning #
stateDiagram-v2
%% ===== CLASS DEFINITIONS (Math-based colours) =====
classDef algebra fill:#cfe8ff,stroke:#1e3a8a,stroke-width:1px
classDef probability fill:#d1fae5,stroke:#065f46,stroke-width:1px
classDef geometry fill:#ffedd5,stroke:#9a3412,stroke-width:1px
classDef logic fill:#ede9fe,stroke:#5b21b6,stroke-width:1px
classDef category font-style:italic,font-weight:bold,fill:#aaaaaa,stroke:#374151,stroke-width:3px
%% ===== ROOT =====
ML: Machine Learning
%% ===== SUPERVISED =====
ML --> SL:::category
SL: Supervised Learning
SL --> Classification
Classification --> NB:::probability
NB: Naive Bayes
NB --> KNN:::geometry
KNN: k-Nearest Neighbours
KNN --> SVM:::algebra
SVM: Support Vector Machine
SL --> Regression
Regression --> LR:::algebra
LR: Linear Regression
LR --> NN:::algebra
NN: Neural Network
NN --> DT:::logic
DT: Decision Tree
%% ===== UNSUPERVISED =====
ML --> USL:::category
USL: Unsupervised Learning
USL --> Clustering
Clustering --> KM:::geometry
KM: K-Means
KM --> GMM:::probability
GMM: Gaussian Mixture Model
GMM --> HMM:::probability
HMM: Hidden Markov Model
%% ===== REINFORCEMENT =====
ML --> RL:::category
RL: Reinforcement Learning
RL --> DM:::logic
DM: Decision Making
Mathematical Legend
Algebra / Linear Algebra (Blue) #
Used heavily when models rely on:
- Equations
- Vectors
- Hyperplanes
- Weights
Examples:
- Linear Regression
- Neural Networks
- Support Vector Machines (SVM)
Probability & Statistics (Green) #
Used when models are based on:
- Likelihood
- Distributions
- Randomness
- Bayesian thinking
Examples:
- Naive Bayes
- Gaussian Mixture Models (GMM)
- Hidden Markov Models (HMM)
Geometry / Distance (Orange) #
Used when models depend on:
- Distance
- Similarity
- Clustering in space
Examples:
- k-Nearest Neighbours (k-NN)
- K-Means
Logic / Decision / Optimisation (Purple) #
Used when models are based on:
- Rules
- Decisions
- Reward and punishment
- Tree structures
Examples:
Decision Trees
Reinforcement Learning