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.
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├── 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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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.
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Complex Eigenvalue Art -- Harvest complex eigenvalues from random matrices, make something funky...
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Least Squares Modeling and Analysis -- Takes mock data, builds a Least Squares model, visualizes and evaluates the results.
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QR Decomposition: Manipulation, Inverse, Validation -- Take the Identity Matrix and add/subtract a Rank 1 Matrix (formed from two vectors) of -- a.k.a. a Rank-1 Update. Factor with QR decomposition, reconstruct, verify the inverse. Run numerical and visual checks.
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Gram-Schmidt Procedure -- Generating an orthonormal matrix manually through sequential vector projection and column vector subtraction.
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PDFs vs. CDFs -- Building intuition around a probability density function and its cumulative distribution function through visualization.
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Probability vs Odds -- Interactive explanation of odds vs probability, useful for classification models.
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Z-scores vs Trimmed Means -- Compare robustness of standard vs trimmed methods in outlier removal.
- 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 Software Development, Mathematics, and AI Saftey Theory.
- Studying since 2022 -- developing fluency in Python and progressing rapidly in JavaScript and
$\Rightarrow$ math, data science and full-stack dev. - 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: andrewblais.dev