# anishiisc/ml_math architecture

> Architecture diagram of the GitHub repository anishiisc/ml_math, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-11-14). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/anishiisc/ml_math
- Repository: https://github.com/anishiisc/ml_math

## Overview

This repository is a collection of machine-learning and related study materials, not an executable application. Its user-facing activity is reading notes and presentations; there is no source code or README documenting a runtime workflow or relationships between materials. The graph groups the files by topic, based on their filenames, and shows no edges because the available evidence does not establish interactions.

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_foundations["Math Foundations"]
  node_linear_algebra["Linear Algebra"]
  node_svd["SVD<br/>[SVD.pdf]"]
  node_grad_descent["Gradient Descent<br/>[Grad_Descent.pptx]"]
end

subgraph group_statistics["Statistical Methods"]
  node_ols["OLS Theory<br/>[OLS_Theory.pdf]"]
  node_mlr["Multiple Linear Regression<br/>[MLR.pdf]"]
  node_logistic["Logistic Regression"]
  node_multinomial["Multinomial Logit"]
  node_consumer_choice["Consumer Choice"]
  node_regularization["Regularization<br/>[Regularization.pdf]"]
end

subgraph group_models["Predictive Models"]
  node_decision_tree["Decision Trees"]
  node_naive_bayes["Naive Bayes"]
  node_svm_set1["SVM Set 1<br/>[SVM_Set_1.pdf]"]
  node_svm_set2["SVM Set 2<br/>[SVM_Set_2.pdf]"]
  node_boosting["Boosting Math"]
  node_gbm["Gradient Boosting<br/>[GBM_notes.pdf]"]
end

subgraph group_neural["Neural Networks"]
  node_ann["ANN Notes"]
  node_backprop["Backpropagation"]
  node_ffn["Feedforward Networks<br/>[FFN_Notes.pdf]"]
end

click node_linear_algebra "https://github.com/anishiisc/ml_math/blob/main/Linear_Algebra.pptx"
click node_svd "https://github.com/anishiisc/ml_math/blob/main/SVD.pdf"
click node_grad_descent "https://github.com/anishiisc/ml_math/blob/main/Grad_Descent.pptx"
click node_ols "https://github.com/anishiisc/ml_math/blob/main/OLS_Theory.pdf"
click node_mlr "https://github.com/anishiisc/ml_math/blob/main/MLR.pdf"
click node_logistic "https://github.com/anishiisc/ml_math/blob/main/Logistics_Regression.pdf"
click node_multinomial "https://github.com/anishiisc/ml_math/blob/main/Multinomial_logit.pdf"
click node_consumer_choice "https://github.com/anishiisc/ml_math/blob/main/Consumer_Choice_Theory_Shelf_Reccomendations.pdf"
click node_regularization "https://github.com/anishiisc/ml_math/blob/main/Regularization.pdf"
click node_decision_tree "https://github.com/anishiisc/ml_math/blob/main/Decision%20_Tree%20Tutorial%20.pptx"
click node_naive_bayes "https://github.com/anishiisc/ml_math/blob/main/Naive_Baiyes_Notes.pdf"
click node_svm_set1 "https://github.com/anishiisc/ml_math/blob/main/SVM_Set_1.pdf"
click node_svm_set2 "https://github.com/anishiisc/ml_math/blob/main/SVM_Set_2.pdf"
click node_boosting "https://github.com/anishiisc/ml_math/blob/main/Boosting_Math_Part_1.pdf"
click node_gbm "https://github.com/anishiisc/ml_math/blob/main/GBM_notes.pdf"
click node_ann "https://github.com/anishiisc/ml_math/blob/main/ANN_Scanned_Notes.pdf"
click node_backprop "https://github.com/anishiisc/ml_math/blob/main/BackPropagation_Worked_Example.pdf"
click node_ffn "https://github.com/anishiisc/ml_math/blob/main/FFN_Notes.pdf"

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_linear_algebra,node_svd,node_grad_descent toneBlue
class node_ols,node_mlr,node_logistic,node_multinomial,node_consumer_choice,node_regularization toneAmber
class node_decision_tree,node_naive_bayes,node_svm_set1,node_svm_set2,node_boosting,node_gbm toneMint
class node_ann,node_backprop,node_ffn toneRose
```

## Components

### Math Foundations

- [Linear Algebra](https://github.com/anishiisc/ml_math/blob/main/Linear_Algebra.pptx): `Linear_Algebra.pptx`
- [SVD](https://github.com/anishiisc/ml_math/blob/main/SVD.pdf): `SVD.pdf`
- [Gradient Descent](https://github.com/anishiisc/ml_math/blob/main/Grad_Descent.pptx): `Grad_Descent.pptx`

### Statistical Methods

- [OLS Theory](https://github.com/anishiisc/ml_math/blob/main/OLS_Theory.pdf): `OLS_Theory.pdf`
- [Multiple Linear Regression](https://github.com/anishiisc/ml_math/blob/main/MLR.pdf): `MLR.pdf`
- [Logistic Regression](https://github.com/anishiisc/ml_math/blob/main/Logistics_Regression.pdf): `Logistics_Regression.pdf`
- [Multinomial Logit](https://github.com/anishiisc/ml_math/blob/main/Multinomial_logit.pdf): `Multinomial_logit.pdf`
- [Consumer Choice](https://github.com/anishiisc/ml_math/blob/main/Consumer_Choice_Theory_Shelf_Reccomendations.pdf): `Consumer_Choice_Theory_Shelf_Reccomendations.pdf`
- [Regularization](https://github.com/anishiisc/ml_math/blob/main/Regularization.pdf): `Regularization.pdf`

### Predictive Models

- [Decision Trees](https://github.com/anishiisc/ml_math/blob/main/Decision%20_Tree%20Tutorial%20.pptx): `Decision _Tree Tutorial .pptx`
- [Naive Bayes](https://github.com/anishiisc/ml_math/blob/main/Naive_Baiyes_Notes.pdf): `Naive_Baiyes_Notes.pdf`
- [SVM Set 1](https://github.com/anishiisc/ml_math/blob/main/SVM_Set_1.pdf): `SVM_Set_1.pdf`
- [SVM Set 2](https://github.com/anishiisc/ml_math/blob/main/SVM_Set_2.pdf): `SVM_Set_2.pdf`
- [Boosting Math](https://github.com/anishiisc/ml_math/blob/main/Boosting_Math_Part_1.pdf): `Boosting_Math_Part_1.pdf`
- [Gradient Boosting](https://github.com/anishiisc/ml_math/blob/main/GBM_notes.pdf): `GBM_notes.pdf`

### Neural Networks

- [ANN Notes](https://github.com/anishiisc/ml_math/blob/main/ANN_Scanned_Notes.pdf): `ANN_Scanned_Notes.pdf`
- [Backpropagation](https://github.com/anishiisc/ml_math/blob/main/BackPropagation_Worked_Example.pdf): `BackPropagation_Worked_Example.pdf`
- [Feedforward Networks](https://github.com/anishiisc/ml_math/blob/main/FFN_Notes.pdf): `FFN_Notes.pdf`