This repository is a learning journal based on the book Statistical Machine Learning for Astronomy by Yuan-Sen Ting. It includes :

• Conceptual summaries and notes on key statistical and machine learning ideas presented in the book
• Python code snippets and implementations of selected methods and examples
• Applications to astronomy, focusing on how statistical techniques can be used to extract insights from astronomical data

The goal of this repo is to reinforce understanding through writing, experimentation, and code — combining theory and practice in one place.

1. Preface and Overview
2. Bayesian Inference
3. Statistical Foundations and Summary Statistics
4. Linear Regression
5. Bayesian Linear Regression
6. Linear Regression with Input Uncertainties
7. Classification and Logistic Regression
8. Multi-Class Classification
9. Bayesian Logistic Regression
10. Principal Component Analysis Fundamentals
11. K-means and Gaussian Mixture Models
12. Sampling and Monte Carlo Methods
13. Markov Chain Monte Carlo
14. Gaussian Processes
15. Neural Networks