A collection of math-related notebooks and scripts paralleling my studies in Linear Algebra, Statistics, and Machine Learning.
This repo reflects homework/practice/projects I worked on as a self-taught developer learning mathematics and applied programming.
It includes structured coursework-based exercises, hands-on problem solving, and exploratory math projects developed independently.
.
├── linearAlgebra/ # Exercises from Linear Algebra coursework
├── statsMachineLearning/ # Exercises from Stats & Machine Learning coursework
├── variousProjects/ # Independent math projects and experiments (Forthcoming!)
├── templates/ # Reusable notebook/script templates for notes
├── requirements.txt # Project dependencies
└── LICENSE # MIT License
These courses emphasize a dual focus on mathematical understanding and Python implementation.
From the most recent Jupyter notebooks:
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Generalized Eigendecomposition: Two Methods -- Analyzing eigenvalues and eigenvectors of
$A v = \lambda B v$ via both direct generalized eigendecompositioneig(A, B)and the equivalent standard eigenproblem form$B^{-1}A v = \lambda v$ . -
Manual Eigendecomposition, 2x2 Case -- Generates a clean random 2x2 matrix. Computes eigenvalues and eigenvectors manually. Verifies the results via
numpy.linalg.eig(). -
Visualizing the Law of Large Numbers and the Central Limit Theorem -- Analyzes several distributions to demonstrate and visualize the law of large numbers and the central limit theorem.
-
Complex Eigenvalue Art -- Harvests complex eigenvalues from random matrices. Makes something funky.
-
Least Squares Modeling and Analysis -- Takes mock data, builds a Least Squares model, visualizes and evaluates the results.
- Math Domains:
- Linear Algebra, Statistics, and Machine Learning fundamentals
- Python Development:
- OOP and modular scripting
- Jupyter Notebooks + LaTeX math rendering
- NumPy, SymPy, Matplotlib, Seaborn, Plotly
- Custom visualizations and exploratory analysis
- Web Development (parallel study):
- Full-Stack JavaScript (Node.js, React, Express)
- Flask (Python)
- SQL/PostgreSQL databases
A self-taught, full-time student focused on Data Science, Mathematics, Software Development, and AI Safety Theory.
Coursework studied since 2022:
- Python for Math, Data Science, Application Development and Web Development.
- Linear Algebra, Statistics, Machine Learning, Calculus and Number Theory, implemented with Python.
- JavaScript (ESM/Express/React) for application development and full-stack web development.
Passionate about AI Alignment and Safety.
Open to internships, junior dev roles, and meaningful collaboration.
Trying to continually learn -- from bootcamps, online documentation/materials/books, and building real things.
This project is licensed under the MIT License.
Andrew Blais – Boston, MA
GitHub: github.com/andrewblais
Website: wateronchair.com