# uthmankontao/machine-learning architecture

> Architecture diagram of the GitHub repository uthmankontao/machine-learning, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-09-25). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/uthmankontao/machine-learning
- Repository: https://github.com/uthmankontao/machine-learning

## Overview

This repository is a collection of machine-learning course materials and practical exercises, not a single application. A learner studies notebooks and implements or explores methods across statistical learning, reinforcement learning, and graph machine learning. The README highlights optimization, regularization, model selection, hyperparameter tuning, AutoML, representation learning, clustering, and factorization; the tree also documents graph algorithms and RL exercises. Sampled source confirms examples for bandits, grid-world planning, maze interaction, graph edit distance, and graph datasets. Many notebook details are unsampled, so their internal wiring is not inferred.

Main entry points: [`ML_graphs/PART IV`](https://github.com/uthmankontao/machine-learning/tree/master/ML_graphs/PART%20IV) (Graph Neural Networks), [`ML_ReinforceLearning/TP3/Maze_generating_interface.py`](https://github.com/uthmankontao/machine-learning/blob/master/ML_ReinforceLearning/TP3/Maze_generating_interface.py) (Maze Design), [`ML_ReinforceLearning/TP3/ForTP3.py`](https://github.com/uthmankontao/machine-learning/blob/master/ML_ReinforceLearning/TP3/ForTP3.py) (Maze RL Exercise), [`ML_advanced/1_Régression Linéaire`](https://github.com/uthmankontao/machine-learning/tree/master/ML_advanced/1_R%C3%A9gression%20Lin%C3%A9aire) (Regression)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_stat["Statistical Learning"]
  node_regression["Regression"]
  node_lasso["LASSO"]
  node_regularization["LASSO and Ridge"]
  node_optimality["Optimality Conditions"]
  node_proximal["Proximal Methods"]
  node_elastic["Elastic Net"]
  node_mmm["MMM Analysis"]
  node_othermethods["Other ML Methods"]
end

subgraph group_rl["Reinforcement Learning"]
  node_bandits["Bandit Learning"]
  node_markov["Markov Decision Processes"]
  node_mazeui["Maze Design"]
  node_mazerl["Maze RL Exercise<br/>[ForTP3.py]"]
end

subgraph group_graphs["Graph Learning"]
  node_graphintro["Graph Introduction"]
  node_graphalgo["Graph Algorithms"]
  node_morphisms["Graph Morphisms"]
  node_gedlsap["GED with LSAP"]
  node_gedastar["GED with A-star"]
  node_embeddings["Graph Embeddings"]
  node_gnn["Graph Neural Networks"]
  node_zfrdata["ZFR Dataset<br/>[ZFRdataset.py]"]
  node_expressivedata["Expressive Dataset<br/>[preparecounting.py]"]
end

node_learner(("Learner"))

node_learner -->|"studies"| node_regression
node_learner -->|"studies"| node_lasso
node_learner -->|"studies"| node_regularization
node_learner -->|"studies"| node_optimality
node_learner -->|"studies"| node_proximal
node_learner -->|"studies"| node_elastic
node_learner -->|"explores"| node_mmm
node_learner -->|"explores"| node_othermethods
node_learner -->|"studies"| node_bandits
node_learner -->|"studies"| node_markov
node_learner -->|"designs maze"| node_mazeui
node_mazeui -->|"provides maze"| node_mazerl
node_learner -->|"explores"| node_mazerl
node_learner -->|"studies"| node_graphintro
node_learner -->|"studies"| node_graphalgo
node_learner -->|"studies"| node_morphisms
node_learner -->|"implements"| node_gedlsap
node_learner -->|"implements"| node_gedastar
node_learner -->|"studies"| node_embeddings
node_learner -->|"studies"| node_gnn
node_gnn -->|"uses dataset"| node_zfrdata
node_gnn -->|"uses dataset"| node_expressivedata

click node_regression "https://github.com/uthmankontao/machine-learning/tree/master/ML_advanced/1_R%C3%A9gression%20Lin%C3%A9aire"
click node_lasso "https://github.com/uthmankontao/machine-learning/tree/master/ML_advanced/2_Lasso"
click node_regularization "https://github.com/uthmankontao/machine-learning/tree/master/ML_advanced/3_Lasso_et_Ridge"
click node_proximal "https://github.com/uthmankontao/machine-learning/tree/master/ML_advanced/5_Lasso_proximal"
click node_elastic "https://github.com/uthmankontao/machine-learning/tree/master/ML_advanced/TP8_ElasticNet"
click node_mmm "https://github.com/uthmankontao/machine-learning/tree/master/ML_advanced/MMM"
click node_othermethods "https://github.com/uthmankontao/machine-learning/tree/master/ML_advanced"
click node_bandits "https://github.com/uthmankontao/machine-learning/tree/master/ML_ReinforceLearning/TP1"
click node_markov "https://github.com/uthmankontao/machine-learning/tree/master/ML_ReinforceLearning/TP2"
click node_mazeui "https://github.com/uthmankontao/machine-learning/blob/master/ML_ReinforceLearning/TP3/Maze_generating_interface.py"
click node_mazerl "https://github.com/uthmankontao/machine-learning/blob/master/ML_ReinforceLearning/TP3/ForTP3.py"
click node_graphintro "https://github.com/uthmankontao/machine-learning/tree/master/ML_graphs/PART%20I/TP1_Introduction"
click node_graphalgo "https://github.com/uthmankontao/machine-learning/tree/master/ML_graphs/PART%20I/TP2_Algorithms"
click node_morphisms "https://github.com/uthmankontao/machine-learning/tree/master/ML_graphs/PART%20I/TP3_Graphs_Morphisms"
click node_gedlsap "https://github.com/uthmankontao/machine-learning/tree/master/ML_graphs/PART%20II/1_Graph%20Edit%20Distance%20with%20LSAP"
click node_gedastar "https://github.com/uthmankontao/machine-learning/tree/master/ML_graphs/PART%20II/2_Astar"
click node_embeddings "https://github.com/uthmankontao/machine-learning/tree/master/ML_graphs/PART%20III"
click node_gnn "https://github.com/uthmankontao/machine-learning/tree/master/ML_graphs/PART%20IV"
click node_zfrdata "https://github.com/uthmankontao/machine-learning/blob/master/ML_graphs/PART%20IV/TP_GNN_challenge_M2/ZFRdataset.py"
click node_expressivedata "https://github.com/uthmankontao/machine-learning/blob/master/ML_graphs/PART%20IV/TP_graph_expressive/preparecounting.py"

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_regression,node_lasso,node_regularization,node_optimality,node_proximal,node_elastic,node_mmm,node_othermethods toneBlue
class node_bandits,node_markov,node_mazeui,node_mazerl toneAmber
class node_graphintro,node_graphalgo,node_morphisms,node_gedlsap,node_gedastar,node_embeddings,node_gnn,node_zfrdata,node_expressivedata toneMint
class node_learner toneIndigo
```

## Components

### Statistical Learning

- [Regression](https://github.com/uthmankontao/machine-learning/tree/master/ML_advanced/1_R%C3%A9gression%20Lin%C3%A9aire): `ML_advanced/1_Régression Linéaire`
- [LASSO](https://github.com/uthmankontao/machine-learning/tree/master/ML_advanced/2_Lasso): `ML_advanced/2_Lasso`
- [LASSO and Ridge](https://github.com/uthmankontao/machine-learning/tree/master/ML_advanced/3_Lasso_et_Ridge): `ML_advanced/3_Lasso_et_Ridge`
- **Optimality Conditions**
- [Proximal Methods](https://github.com/uthmankontao/machine-learning/tree/master/ML_advanced/5_Lasso_proximal): `ML_advanced/5_Lasso_proximal`
- [Elastic Net](https://github.com/uthmankontao/machine-learning/tree/master/ML_advanced/TP8_ElasticNet): `ML_advanced/TP8_ElasticNet`
- [MMM Analysis](https://github.com/uthmankontao/machine-learning/tree/master/ML_advanced/MMM): `ML_advanced/MMM`
- [Other ML Methods](https://github.com/uthmankontao/machine-learning/tree/master/ML_advanced): `ML_advanced`

### Reinforcement Learning

- [Bandit Learning](https://github.com/uthmankontao/machine-learning/tree/master/ML_ReinforceLearning/TP1): `ML_ReinforceLearning/TP1`
- [Markov Decision Processes](https://github.com/uthmankontao/machine-learning/tree/master/ML_ReinforceLearning/TP2): `ML_ReinforceLearning/TP2`
- [Maze Design](https://github.com/uthmankontao/machine-learning/blob/master/ML_ReinforceLearning/TP3/Maze_generating_interface.py): `ML_ReinforceLearning/TP3/Maze_generating_interface.py`
- [Maze RL Exercise](https://github.com/uthmankontao/machine-learning/blob/master/ML_ReinforceLearning/TP3/ForTP3.py): `ML_ReinforceLearning/TP3/ForTP3.py`

### Graph Learning

- [Graph Introduction](https://github.com/uthmankontao/machine-learning/tree/master/ML_graphs/PART%20I/TP1_Introduction): `ML_graphs/PART I/TP1_Introduction`
- [Graph Algorithms](https://github.com/uthmankontao/machine-learning/tree/master/ML_graphs/PART%20I/TP2_Algorithms): `ML_graphs/PART I/TP2_Algorithms`
- [Graph Morphisms](https://github.com/uthmankontao/machine-learning/tree/master/ML_graphs/PART%20I/TP3_Graphs_Morphisms): `ML_graphs/PART I/TP3_Graphs_Morphisms`
- [GED with LSAP](https://github.com/uthmankontao/machine-learning/tree/master/ML_graphs/PART%20II/1_Graph%20Edit%20Distance%20with%20LSAP): `ML_graphs/PART II/1_Graph Edit Distance with LSAP`
- [GED with A-star](https://github.com/uthmankontao/machine-learning/tree/master/ML_graphs/PART%20II/2_Astar): `ML_graphs/PART II/2_Astar`
- [Graph Embeddings](https://github.com/uthmankontao/machine-learning/tree/master/ML_graphs/PART%20III): `ML_graphs/PART III`
- [Graph Neural Networks](https://github.com/uthmankontao/machine-learning/tree/master/ML_graphs/PART%20IV): `ML_graphs/PART IV`
- [ZFR Dataset](https://github.com/uthmankontao/machine-learning/blob/master/ML_graphs/PART%20IV/TP_GNN_challenge_M2/ZFRdataset.py): `ML_graphs/PART IV/TP_GNN_challenge_M2/ZFRdataset.py`
- [Expressive Dataset](https://github.com/uthmankontao/machine-learning/blob/master/ML_graphs/PART%20IV/TP_graph_expressive/preparecounting.py): `ML_graphs/PART IV/TP_graph_expressive/preparecounting.py`

### Other components

- **Learner**

## Connections

- Learner → Regression: studies
- Learner → LASSO: studies
- Learner → LASSO and Ridge: studies
- Learner → Optimality Conditions: studies
- Learner → Proximal Methods: studies
- Learner → Elastic Net: studies
- Learner → MMM Analysis: explores
- Learner → Other ML Methods: explores
- Learner → Bandit Learning: studies
- Learner → Markov Decision Processes: studies
- Learner → Maze Design: designs maze
- Maze Design → Maze RL Exercise: provides maze
- Learner → Maze RL Exercise: explores
- Learner → Graph Introduction: studies
- Learner → Graph Algorithms: studies
- Learner → Graph Morphisms: studies
- Learner → GED with LSAP: implements
- Learner → GED with A-star: implements
- Learner → Graph Embeddings: studies
- Learner → Graph Neural Networks: studies
- Graph Neural Networks → ZFR Dataset: uses dataset
- Graph Neural Networks → Expressive Dataset: uses dataset