My Notes
Welcome 👋
This repository is a living notebook where I organize my understanding of core Computer Science and Data Science concepts—built while studying, revising, and questioning why things work the way they do.
I am still in the process of adding more things to this. These notes are intended for GATE aspirants and anyone currently pursuing the IIT Madras BS in Data Science and Applications Degree. Reach out to me at sly.of.zero@gmail.com if you want to collaborate in this alongside me! Cheers 🍻!
The notes may have same LaTeX rendering issues due to importing Obsidian files into Quartz. I apologize if you encounter any, please reach out to me or raise an issue in the Github repo and I’d try to fix it at the earliest.
Currently working on adding more notes for -
- Algorithms
- Discrete Mathematics
- Mathematical foundations of Machine Learning
- Data Structures
- Theory of Computation
- Compiler Design
- Mathematical foundations of Generative AI
🛑 Disclaimer - DO NOT consider these notes as your single source of truth. I have tried my best in keeping the concepts covered as comprehensive and streamlined as possible. But due to the lack of any secondary proof-reader other than myself, there can be some leaps in logic and use of wrong terminology. I have paid utmost attention in order of avoid such issues but it is very possible that I might have missed some. Always cross-check once from other reliable sources as well.
These notes are continuously refined as my understanding improves.
🔢 Discrete Mathematics
The language behind algorithms.
Includes:
- Logic, sets, relations, and functions
- Combinatorics and counting arguments
- Recurrences, proofs, and asymptotic reasoning
- Graphs, trees, and their properties
📂 Start here → Discrete Maths
📐 Mathematical Foundations of Machine Learning I
The fundamentals of machine learning.
Includes:
📂 Start here → Mathematical Foundations of Machine Learning I
📘 Algorithms
How problems are solved.
Includes:
- Divide & conquer, greedy, dynamic programming
- Time and space complexity analysis
- Recurrence relations and their solutions
- Correctness arguments and trade-offs
📂 Start here → Algorithms
🧱 Data Structures
How data is organized.
Includes:
- Linear and non-linear structures
- Internal representations and memory layout
- Operation costs and invariants
- When not to use a structure
📂 Start here → Data Structures
⚙️ Computer Organization and Architecture
How a computer works.
Includes:
- Control Unit, Instructions, Registers, Addressing Modes
- I/O Organization
- Cache Organization
- Magnetic Disks
- Pipeline Processing
📂 Start here → Computer Organization and Architecture
🪟 Mathematical Foundations of Generative AI
The theory that explains why generative models work.
Includes:
- Generative Adversarial Networks (GANs)
- Variational Auto Encoders (VAEs)
- Denoising Diffusion Probabilistic Models (DDPMs)
- Auto Regressive Models (AR)
- State Space Models (SSMs)
- RL-based Alignment for LLMs
📂 Start here → Mathematical Foundations of Generative AI
“An algorithm is not just a procedure — it is a proof that a problem can be solved.”