Learning Materials

• ⭐ GitHub Starred Repositories
  A curated list of GitHub repositories I’ve starred—covering R packages, statistical modeling tools, and machine learning utilities that I use or recommend.

• 📘 Convex Optimization (CMU)
  A comprehensive graduate-level course by Prof. Ryan Tibshirani, offering lecture notes, assignments, and projects on convex optimization—critical for machine learning and statistical modeling.

• 📊 An Introduction to Statistical Learning
  By Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani. This resource includes a free PDF of the book and R code examples covering key topics in supervised learning.

• 🧮 Combinatorics Lecture Notes by Prof. Stephan Wagner
  A set of clear and well-organized lecture notes on combinatorics—ideal for sharpening your skills in enumeration, graph theory, and advanced counting techniques.

• 📦 Creating an R Package (MIT Tutorial)
  A step-by-step guide by In Song Kim on building, documenting, and sharing your own R packages—great for researchers developing reusable statistical tools.

• 🧬 Bioinformatics and Bioconductor Tutorials
  Hands-on materials for analyzing high-throughput genomic data in R using Bioconductor packages. Topics include RNA-seq, methylation analysis, and more.