# pascalegourdeau/comp-551 architecture

> Architecture diagram of the GitHub repository pascalegourdeau/comp-551, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-09-28). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/pascalegourdeau/comp-551
- Repository: https://github.com/pascalegourdeau/comp-551

## Overview

This repository is a collection of COMP551 applied machine-learning teaching notebooks, not a single application with a shared execution pipeline. A learner explores the notebooks directly; the README documents their subject areas, but notebook implementations were not sampled, so the graph does not infer code-level calls or dependencies. The groups preserve the documented coverage, including inference, supervised methods, training and evaluation, deep learning, and nonparametric and unsupervised learning.

Main entry points: [`Gaussian.ipynb`](https://github.com/pascalegourdeau/comp-551/blob/main/Gaussian.ipynb) (Gaussian), [`MLE_BayesianInference.ipynb`](https://github.com/pascalegourdeau/comp-551/blob/main/MLE_BayesianInference.ipynb) (MLE & Bayesian), [`NaiveBayes.ipynb`](https://github.com/pascalegourdeau/comp-551/blob/main/NaiveBayes.ipynb) (Naive Bayes), [`LinearRegression.ipynb`](https://github.com/pascalegourdeau/comp-551/blob/main/LinearRegression.ipynb) (Linear Regression)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_inference["Inference"]
  node_gaussian["Gaussian<br/>[Gaussian.ipynb]"]
  node_mle_bayes["MLE &amp; Bayesian"]
end

subgraph group_supervised["Supervised Learning"]
  node_naive_bayes["Naive Bayes<br/>[NaiveBayes.ipynb]"]
  node_linear_regression["Linear Regression"]
  node_logistic_regression["Logistic Regression"]
  node_perceptron_svm["Perceptron &amp; SVM"]
end

subgraph group_training["Training &amp; Evaluation"]
  node_gradient_descent["Gradient Descent"]
  node_regularization["Regularization"]
  node_model_selection["Model Selection"]
  node_curse_dimensionality["Curse of Dimensionality"]
end

subgraph group_deep["Deep Networks"]
  node_mlp["MLP &amp; Autodiff<br/>[MLP.ipynb]"]
  node_convnets["Convolutional Networks<br/>[ConvNets.ipynb]"]
  node_rnn["Recurrent Networks<br/>[RNN.ipynb]"]
end

subgraph group_other_methods["Other Methods"]
  node_knn["Nearest Neighbours<br/>[KNN.ipynb]"]
  node_decision_tree["Trees &amp; Forests<br/>[DecisionTree.ipynb]"]
  node_kmeans["K-Means"]
  node_em_gmm["EM for Mixtures"]
  node_pca["PCA<br/>[PCA.ipynb]"]
end

node_learner(("Learner"))

node_learner -->|"studies"| node_gaussian
node_learner -->|"studies"| node_mle_bayes
node_learner -->|"studies"| node_naive_bayes
node_learner -->|"studies"| node_linear_regression
node_learner -->|"studies"| node_logistic_regression
node_learner -->|"studies"| node_perceptron_svm
node_learner -->|"studies"| node_gradient_descent
node_learner -->|"studies"| node_regularization
node_learner -->|"studies"| node_model_selection
node_learner -->|"studies"| node_curse_dimensionality
node_learner -->|"studies"| node_mlp
node_learner -->|"studies"| node_convnets
node_learner -->|"studies"| node_rnn
node_learner -->|"studies"| node_knn
node_learner -->|"studies"| node_decision_tree
node_learner -->|"studies"| node_kmeans
node_learner -->|"studies"| node_em_gmm
node_learner -->|"studies"| node_pca

click node_gaussian "https://github.com/pascalegourdeau/comp-551/blob/main/Gaussian.ipynb"
click node_mle_bayes "https://github.com/pascalegourdeau/comp-551/blob/main/MLE_BayesianInference.ipynb"
click node_naive_bayes "https://github.com/pascalegourdeau/comp-551/blob/main/NaiveBayes.ipynb"
click node_linear_regression "https://github.com/pascalegourdeau/comp-551/blob/main/LinearRegression.ipynb"
click node_logistic_regression "https://github.com/pascalegourdeau/comp-551/blob/main/LogisticRegression.ipynb"
click node_perceptron_svm "https://github.com/pascalegourdeau/comp-551/blob/main/Perceptron_and_LinearSVM.ipynb"
click node_gradient_descent "https://github.com/pascalegourdeau/comp-551/blob/main/GradientDescent.ipynb"
click node_regularization "https://github.com/pascalegourdeau/comp-551/blob/main/Regularization.ipynb"
click node_model_selection "https://github.com/pascalegourdeau/comp-551/blob/main/ModelSelection.ipynb"
click node_curse_dimensionality "https://github.com/pascalegourdeau/comp-551/blob/main/CurseOfDimensionality.ipynb"
click node_mlp "https://github.com/pascalegourdeau/comp-551/blob/main/MLP.ipynb"
click node_convnets "https://github.com/pascalegourdeau/comp-551/blob/main/ConvNets.ipynb"
click node_rnn "https://github.com/pascalegourdeau/comp-551/blob/main/RNN.ipynb"
click node_knn "https://github.com/pascalegourdeau/comp-551/blob/main/KNN.ipynb"
click node_decision_tree "https://github.com/pascalegourdeau/comp-551/blob/main/DecisionTree.ipynb"
click node_kmeans "https://github.com/pascalegourdeau/comp-551/blob/main/KMeansClustering.ipynb"
click node_em_gmm "https://github.com/pascalegourdeau/comp-551/blob/main/EMforGaussianMixture.ipynb"
click node_pca "https://github.com/pascalegourdeau/comp-551/blob/main/PCA.ipynb"

classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_gaussian,node_mle_bayes toneBlue
class node_naive_bayes,node_linear_regression,node_logistic_regression,node_perceptron_svm toneAmber
class node_gradient_descent,node_regularization,node_model_selection,node_curse_dimensionality toneMint
class node_mlp,node_convnets,node_rnn toneRose
class node_knn,node_decision_tree,node_kmeans,node_em_gmm,node_pca,node_learner toneIndigo
```

## Components

### Inference

- [Gaussian](https://github.com/pascalegourdeau/comp-551/blob/main/Gaussian.ipynb): `Gaussian.ipynb`
- [MLE & Bayesian](https://github.com/pascalegourdeau/comp-551/blob/main/MLE_BayesianInference.ipynb): `MLE_BayesianInference.ipynb`

### Supervised Learning

- [Naive Bayes](https://github.com/pascalegourdeau/comp-551/blob/main/NaiveBayes.ipynb): `NaiveBayes.ipynb`
- [Linear Regression](https://github.com/pascalegourdeau/comp-551/blob/main/LinearRegression.ipynb): `LinearRegression.ipynb`
- [Logistic Regression](https://github.com/pascalegourdeau/comp-551/blob/main/LogisticRegression.ipynb): `LogisticRegression.ipynb`
- [Perceptron & SVM](https://github.com/pascalegourdeau/comp-551/blob/main/Perceptron_and_LinearSVM.ipynb): `Perceptron_and_LinearSVM.ipynb`

### Training & Evaluation

- [Gradient Descent](https://github.com/pascalegourdeau/comp-551/blob/main/GradientDescent.ipynb): `GradientDescent.ipynb`
- [Regularization](https://github.com/pascalegourdeau/comp-551/blob/main/Regularization.ipynb): `Regularization.ipynb`
- [Model Selection](https://github.com/pascalegourdeau/comp-551/blob/main/ModelSelection.ipynb): `ModelSelection.ipynb`
- [Curse of Dimensionality](https://github.com/pascalegourdeau/comp-551/blob/main/CurseOfDimensionality.ipynb): `CurseOfDimensionality.ipynb`

### Deep Networks

- [MLP & Autodiff](https://github.com/pascalegourdeau/comp-551/blob/main/MLP.ipynb): `MLP.ipynb`
- [Convolutional Networks](https://github.com/pascalegourdeau/comp-551/blob/main/ConvNets.ipynb): `ConvNets.ipynb`
- [Recurrent Networks](https://github.com/pascalegourdeau/comp-551/blob/main/RNN.ipynb): `RNN.ipynb`

### Other Methods

- [Nearest Neighbours](https://github.com/pascalegourdeau/comp-551/blob/main/KNN.ipynb): `KNN.ipynb`
- [Trees & Forests](https://github.com/pascalegourdeau/comp-551/blob/main/DecisionTree.ipynb): `DecisionTree.ipynb`
- [K-Means](https://github.com/pascalegourdeau/comp-551/blob/main/KMeansClustering.ipynb): `KMeansClustering.ipynb`
- [EM for Mixtures](https://github.com/pascalegourdeau/comp-551/blob/main/EMforGaussianMixture.ipynb): `EMforGaussianMixture.ipynb`
- [PCA](https://github.com/pascalegourdeau/comp-551/blob/main/PCA.ipynb): `PCA.ipynb`

### Other components

- **Learner**

## Connections

- Learner → Gaussian: studies
- Learner → MLE & Bayesian: studies
- Learner → Naive Bayes: studies
- Learner → Linear Regression: studies
- Learner → Logistic Regression: studies
- Learner → Perceptron & SVM: studies
- Learner → Gradient Descent: studies
- Learner → Regularization: studies
- Learner → Model Selection: studies
- Learner → Curse of Dimensionality: studies
- Learner → MLP & Autodiff: studies
- Learner → Convolutional Networks: studies
- Learner → Recurrent Networks: studies
- Learner → Nearest Neighbours: studies
- Learner → Trees & Forests: studies
- Learner → K-Means: studies
- Learner → EM for Mixtures: studies
- Learner → PCA: studies