MIT-Style • Problem-First • Concept-Driven
This repository documents my long-term, structured self-study in mathematics, computing, and engineering, guided primarily by MIT OpenCourseWare and standard university textbooks.
The focus is not on certificates or course completion, but on deep conceptual understanding, rigorous problem-solving, and the ability to apply theory to real systems.
My approach is inspired by:
- MIT’s emphasis on fundamentals
- CS50’s problem-first methodology
- The Feynman technique of learning through explanation
Key principles:
- Learn concepts as systems, not formulas
- Treat mathematics like logic and algorithms
- Prefer derivations over memorization
- Use computation and visualization only to verify reasoning
- Build intuition strong enough to work without tools
Each folder corresponds to a topic, course, or textbook chapter, typically including:
- A focused
README.mddescribing the learning goal - Problem-solving notes and derivations
- Conceptual explanations written in my own words
- Occasional applied or mini-projects
The structure evolves as understanding deepens.
- MIT OpenCourseWare
- 18.01 — Single Variable Calculus
- 18.02 — Multivariable Calculus
- 6.xx — Computer Science & Systems
- Standard textbooks (e.g., James Stewart, Spivak, Strang)
- Original problem sets and self-designed exercises
- Paper-first problem solving
- LaTeX / Markdown for documentation
- Programming used only when it adds insight
- Version control used as a learning log, not just storage
To build a foundation strong enough to:
- Reason independently about mathematical systems
- Apply mathematics to computing, engineering, and modeling
- Transition smoothly into advanced topics such as:
- Algorithms
- Numerical methods
- Machine learning
- Quantitative modeling
This repository reflects an active learning process.
Expect incomplete sections, revisions, and evolving explanations as understanding improves.
Accuracy and clarity take priority over polish.
This repository exists to learn deeply, not quickly.