ML-Codes

This repo contains PyTorch implementations of many traditional machine learning algorithms, deep learning algorithms, and reinforcement learning algorithms.

For some algorithms based on strict mathematical formulas, it is advisable to maintain the consistency between the code parameters and the mathematical symbols.

Structure

ML-codes/
├── Classification
│   ├── Adaboost
│   ├── Forests
│   ├── KNN
│   ├── Logistic
│   ├── Softmax
│   ├── SVM
│   └── Tree
├── Data
│   └── phd
├── NN
│   ├── AE
│   ├── CNN
│   ├── Diffusion
│   ├── GAN
│   ├── MLP
│   ├── PointNet
│   ├── RNN
│   ├── Transformer
│   ├── UNet
│   └── VAE
├── Regression
│   ├── GP
│   ├── Lasso
│   ├── Linear
│   └── Ridge
├── RL
│   ├── PPO
│   └── TRPO
└── Unsupervised
    ├── K-means
    ├── MoG
    └── PCA

Environment

conda create -n ml python=3.11 -y
conda activate ml
pip install -r requirements.txt

# version due to your device
pip3 install torch torchvision --index-url https://download.pytorch.org/whl/cu121

References

• https://github.com/MuLabPKU/MachineLearningCourse2025
• https://pku-epic.github.io/Intro2CV_2026/
• https://github.com/luokn/ml
• https://github.com/milesial/Pytorch-UNet