📐 Mathematics for Machine Learning

A personal learning repository documenting my journey through the essential mathematics behind machine learning — covering linear algebra, calculus, and statistics with hands-on Jupyter notebooks.

📁 Project Structure

ml-math/
├── linear_algebra/       # Linear algebra concepts and exercises
│   ├── vectors.ipynb
│   ├── vector_spaces.ipynb
│   └── vector_multiplication.ipynb
├── statistics/            # Statistics concepts and exercises
│   ├── descriptive_statistics.ipynb
│   └── visualizing_data.ipynb
├── pyproject.toml
└── README.md

📚 Topics

Linear Algebra

Notebook	Topics Covered
vectors.ipynb	Geometric & algebraic interpretation, vector orientation, transpose, addition & subtraction, scalar multiplication
vector_spaces.ipynb	Vector spaces, subspaces, span, linear independence, basis
vector_multiplication.ipynb	Dot product, vector multiplication operations

Statistics

Notebook	Topics Covered
descriptive_statistics.ipynb	Descriptive vs inferential statistics, data distribution, measures of central tendency
visualizing_data.ipynb	Data visualization, bar plots, pie charts

🛠️ Setup

This project uses uv for dependency management and requires Python 3.14+.

Dependencies

• NumPy
• Matplotlib
• Pandas
• SciPy
• Statsmodels
• IPyKernel (for Jupyter support)

Installation

# Clone the repository
git clone https://github.com/hayohtee/ml-math.git
cd ml-math

# Install dependencies with uv
uv sync

Running Notebooks

# Launch Jupyter
uv run jupyter notebook

📝 License

This project is for personal educational purposes.