How anishiisc/ml_math works
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.
Components 18
Math Foundations
- Linear Algebra
Linear_Algebra.pptx - SVD
SVD.pdf - Gradient Descent
Grad_Descent.pptx
Statistical Methods
- OLS Theory
OLS_Theory.pdf - Multiple Linear Regression
MLR.pdf - Logistic Regression
Logistics_Regression.pdf - Multinomial Logit
Multinomial_logit.pdf - Consumer Choice
Consumer_Choice_Theory_Shelf_Reccomendations.pdf - Regularization
Regularization.pdf
Predictive Models
- Decision Trees
Decision _Tree Tutorial .pptx - Naive Bayes
Naive_Baiyes_Notes.pdf - SVM Set 1
SVM_Set_1.pdf - SVM Set 2
SVM_Set_2.pdf - Boosting Math
Boosting_Math_Part_1.pdf - Gradient Boosting
GBM_notes.pdf
Neural Networks
- ANN Notes
ANN_Scanned_Notes.pdf - Backpropagation
BackPropagation_Worked_Example.pdf - Feedforward Networks
FFN_Notes.pdf