• ## Notes on Machine Learning 2: Decision trees

(ML 2.1) Classification trees (CART) CART (Classification And Regression Trees) by Breiman et. al. (see: https://rafalab.github.io/pages/649/section-11.pdf) Conceptually very simple approach to classification and regression. Can be extremely powerful, specially coupled with some randomizaiton technique, and essentially give the best performance. Main idea: Form a binary tree (by binary splits), and...

• ## Notes on Deep Learning (Book)

In this page I summarize in a succinct and straighforward fashion what I learn from the book Deep Learning by Ian Goodfellow, Yoshua Bengio and Aaron Courville, along with my own thoughts and related resources. I will update this page frequently, like every week, until it’s complete. Acronyms DL: Deep...

• ## Notes on Convex Optimization

In this page I summarize in a succinct and straighforward fashion what I learn from Convex Optimization course by Stephen Boyd, along with my own thoughts and related resources. I will update this page frequently, like every week, until it’s complete. Acronyms LA: Linear Algebra Preceding materials to be added.....

• ## Notes on Probability Primer 1: Measure theory

(PP 1.1) Measure theory: Why measure theory - The Banach-Tarski Paradox Why measure theory? A bit more detailed explanations on the Banach-Tarski paradox here: The Banach–Tarski Paradox. (PP 1.2) Measure theory: $\sigma$-algebras Definition. Given a set $\Omega$, a $\sigma$-algebra on $\Omega$ is a nonempty collection $\mathcal{A} \subset 2^{\Omega}$ s.t. closed...

• ## Notes on Probability Primer (master page)

This is the master page for Notes on Machine Learning posts, in which I summarize in a succinct and straighforward fashion what I learn from Probability Primer course by Mathematical Monk, along with my own thoughts and related resources. Acronyms RV: random variable Notes on Probability Primer 1: Measure theory...