Posts

  • Feature Selection: A Primer

    My shot at explaining the statistical intuition behind the most common filter methods in Data Science and ML.
  • Recommender Systems

    My college notes on recommender systems, including the making recommendations, collaborative filtering, content-based filtering, deep learning for content-based filtering and more.
  • Principal Component Analysis

    These are my notes on Principal Component Analysis (PCA), with the algorithm and implementation in Python.
  • Notes on Probability Theory

    My college notes on probability theory, including discrete/continuous random variables and distributions, conditional probability and independence, expectation, limits, random walks and more. Most of the notes are from the book 'Probability with Applications and R' by Wagaman and Dobrow. If you have the chance, I highly recommend reading the book or going through its problems.