AI Reading List: Technologies: Machine Learning

Below is a portion of my informal list of readings related to Artificial Intelligence (AI). This started out as a very short list created for use in conjunction with an academic presentation and has now grown much larger. Please let me know if you have any corrections, additions, suggestions, etc. It is very idiosyncratic and not meant to be comprehensive. Please feel free to share with others.

Artificial Intelligence (AI) Reading List, by Philip Rubin

Technologies — Machine Learning

Basak Kaya. Machine Learning Concepts Explained #1: Why Machines Need to Learn. Medium, July 1, 2026.

Basak Kaya. Machine Learning Concepts Explained #2: The Four Types of Machine Learning. Medium, July 2, 2026.

Basak Kaya. Machine Learning Concepts Explained #3: The Machine Learning Workflow. Medium, July 3, 2026.

Basak Kaya. Machine Learning Concepts Explained #4: Features and Labels. Medium, July 6, 2026.

Basak Kaya. Machine Learning Concepts Explained #5: Training, Validation, and Testing Datasets. Medium, July 7, 2026.

Basak Kaya. Machine Learning Concepts Explained #6: Overfitting and Underfitting. Medium, July 8, 2026.

Basak Kaya. Machine Learning Concepts Explained #7: Bias-Variance Tradeoff. Medium, July 9, 2026.

Basak Kaya. Machine Learning Concepts Explained #8: Cross-Validation. Medium, July 10, 2026.

Basak Kaya. Machine Learning Concepts Explained #9: Hyperparameter Tuning. Medium, July 12, 2026.

Basak Kaya. Machine Learning Concepts Explained #10: Feature Engineering. Medium, July 13, 2026.

Basak Kaya. The Python Libraries Behind Every Machine Learning Project. Medium, August 6, 2026.

Basak Kaya. Machine Learning Models Explained #1: Linear Regression. Medium, Sep. 26, 2026.

Anupama Bidargaddi. Welcome to The ML Researcher’s Handbook. Medium, Aug. 2, 2026.

Anupama Bidargaddi. Math for ML — Part 1: From Intuition to Mathematics to Code. Medium, Aug. 25, 2026.

Anupama Bidargaddi. Math for ML — Part 2: The Dot Product. Medium, Aug. 26, 2026.

Anupama Bidargaddi. Math for ML — Part 3: From Dot Product to Projection. Medium, Aug. 28, 2026.

Anupama Bidargaddi. Math for ML — Part 4: From Projection to Subspaces. Medium, Aug. 31, 2026.

Anupama Bidargaddi. Math for ML — Part 5: Matrix Transformations — What Does a Matrix Do? Medium, Sep. 7, 2026.

Anupama Bidargaddi. What’s Next in the ML Handbook? Medium, Sep. 7, 2026.

Anupama Bidargaddi. Eigenvalues & Eigenvectors — The Special Directions Hidden Inside a Matrix. Medium, Sep. 16, 2026.

Aaron Krolik and Jacqueline Gu. The Building Blocks Hidden in A.I.: How Tokens Work. The New York Times, Sep. 9, 2026.

Rukshan Pramoditha. Spectral Clustering Explained: How Eigenvectors Reveal Complex Cluster Structures. Understanding Why Spectral Clustering Outperforms K-Means. Medium, Sep. 6, 2026.

Akanksha Verma. The Role of Linear Algebra in Deep Learning. Medium, Sep. 6, 2026.

Sean Moran. Bayesian Networks and Markov Networks: An Intuitive Guide to Structured Uncertainty. Medium, May 30, 2026.

Moez Ben-Azzouz. Math for Machine Learning: The Complete Series. Medium, Jan. 24, 2026.

Kavishka Abeywardana. Probability Theory for Machine Learning: A Beginner’s Tutorial. Medium, December 28, 2025.

Akansha Verma. Linear Regression in ML. Medium, December 28, 2025.

Irene Markelic. Essential Math for Data Science: Matrix Diagonalization Clearly Explained. Medium, December 27, 2025.

Irene Markelic. Unlocking Matrix Secrets! Understanding Eigenvalues and Eigenvectors. Medium, December 18, 2025.

Irene Markelic. Matrix Multiplication Made Easy. Medium, November 16, 2025.

ArnonBonny. 011: Understanding Logistic Regression (Cost Function and Optimization). Medium, November 15, 2025.

Kuriko Awai. Transformer in Action —Optimizing Self-Attention with Attention Approximation. Discover self-attention mechanisms and attention approximation techniques with practical examples. Medium, November 10, 2025.

Maxwell’s Demon. Kalman Filters Demystified — The Algorithm Behind Moon Landings. Medium, November 5, 2025.

Maxwell’s Demon. A Simple (But Not Too Simple) Intro to Linear Estimators. Optimally combining prior knowledge with new data. Medium, September 16, 2025.

Mayur Jain. A Deep Dive into Vector Database Algorithms. Specialized algorithms that enable efficient similarity search on billions of document embeddings. Medium, September 6, 2025.

Okan Yenigün. Recurrent Neural Networks Explained Simply. Memory in Neural Networks: Understanding RNNs. Medium, August 29, 2025.

Ryan Revilla. The First Learning Algorithms: Adaptive Filters. A brief history lesson on machine learning origins that proved to be a useful learning exercise. Medium, August 5, 2025.

Khushbu Shah. 4 Advanced Data Modelling Techniques Every Data Engineer Must Learn. Medium, July 31, 2025.

Rohit Patel. Understanding LLMs from Scratch Using Middle School Math. A self-contained, full explanation to inner workings of an LLM. Medium, Oct. 19, 2024.

Louis Chan. SHAP: Explain Any Machine Learning Model in Python. Your Comprehensive Guide to SHAP, TreeSHAP, and DeepSHAP. Medium, Jan. 11, 2023.

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