Index: Machine Learning

A comprehensive series on machine learning, from foundational algorithms through modern deep learning, large language models, and production deployment. Covers theory, implementation, and connections to applied research.

Articles

Foundations

1. K-Nearest Neighbors
   1. K-Nearest Neighbors
   2. Experiment: MNIST Digit Classification
2. Decision Trees
   1. Decision Trees
   2. Experiment: Heart Disease Prediction
3. Linear Regression
   1. Linear Regression
   2. Gradient Descent
   3. Experiment: Gradient Descent
   4. Practice Problems
4. Logistic Regression & Regularization
   1. Logistic Regression and Regularization

Deep Learning

5. Neural Networks
   1. Neural Networks
6. Backpropagation
   1. Backpropagation

Ensemble Methods

7. Bias-Variance & Bagging
   1. Bias-Variance Tradeoff and Bagging

Statistical Learning

8. Naive Bayes
   1. Naive Bayes
9. Gaussian Discriminant Analysis
   1. Gaussian Discriminant Analysis

Experiments

10. Painting Classifier
    1. Experiment: Painting Classifier
    2. Feature Engineering Pipeline
    3. Model Comparison

Advanced Topics

11. Gradient Boosting
    1. Gradient Boosting
12. Transformers and Attention
    1. Transformers and Attention
13. Large Language Models
    1. Large Language Models
14. Retrieval Augmented Generation
    1. Retrieval Augmented Generation
15. Fine-Tuning and Parameter-Efficient Methods
    1. Fine-Tuning and Parameter-Efficient Methods
16. Model Evaluation and Experiment Design
    1. Model Evaluation and Experiment Design
17. Feature Engineering and Embeddings
    1. Feature Engineering and Embeddings
18. Loss Functions and Optimization
    1. Loss Functions and Optimization

Production ML

19. ML Systems Design
    1. ML Systems Design