Series

Long-form, multi-part explorations. Each one was written as I worked through the material myself.

OMSCS Course Notes

4 parts · Ongoing

Dec 2024 – May 2026

Per-semester summaries from Georgia Tech's OMSCS program — the concepts I want to remember after the class ends.
  1. 1 . ML4T: Machine Learning for Trading Dec 19
  2. 2 . AI4R: Artificial Intelligence for Robotics May 19
  3. 3 . SDP: Software Development Process Dec 19
  4. 4 . KBAI: Knowledge-Based AI May 11

Clean Architecture

6 parts

Jan 2026

Robert C. Martin's principles for designing software that survives change. From programming paradigms to practical implementation and a full case study.
  1. 1 . Design and Programming Paradigms Jan 2
  2. 2 . SOLID Principles Jan 6
  3. 3 . Component Principles Jan 10
  4. 4 . Architecture Design Jan 14
  5. 5 . Architecture Implementation Jan 18
  6. 6 . Details and Case Study Jan 22

Clean Code

4 parts

Dec 2025

Core principles from Robert C. Martin's classic — naming, functions, formatting, error handling, testing, and the road from messy code to clean.
  1. 1 . The Fundamentals Dec 16
  2. 2 . Structure and Formatting Dec 19
  3. 3 . Testing and Class Design Dec 22
  4. 4 . Concurrency and Refactoring Dec 25

Machine Learning (Stanford)

8 parts

Mar 2024 – Apr 2024

Notes from Andrew Ng's ML specialization. Linear and logistic regression, neural networks, decision trees, and unsupervised methods.
  1. 1 . Introduction to Machine Learning Mar 20
  2. 2 . Regression with Multiple Input Variables Mar 20
  3. 3 . Logistic Regression: From Sigmoid to Regularization Mar 24
  4. 4 . Understanding Neural Networks: From Biology to TensorFlow Mar 26
  5. 5 . Training Neural Networks: Activation Functions, Backpropagation, and TensorFlow Implementation Mar 28
  6. 6 . Practical Advice for Applying Machine Learning Apr 1
  7. 7 . Decision Trees and Ensembles: From Splits to Random Forests and XGBoost Apr 3
  8. 8 . Unsupervised Learning: K-means Clustering and Anomaly Detection Apr 9