*This is the overview page for area Mathematics.*

Mathematics is the language of nature. Or at least the best thing that humans have at disposal to understand nature. This is a collection of posts around computer science, machine learning, reinforcement learning and other research-y topics I am involved in.

2020

Dec 28

The Gibbs distribution and general Bayes *ML & Stats * in math

Nov 17

Why is Bayesian inference hard? *ML & Stats * in math

Nov 13

Machine Learning literature *ML & Stats * in math

Nov 10

Integraphs *Uncategorized * in math

Oct 18

Topics in Numerical Linear Algebra *Linear Algebra * in math

Sep 28

Orthogonal projectors and linear regression *Linear Algebra * in math

Aug 30

Discovering Taylor series the hard way *Linear Algebra * in math

Aug 26

Machine Learning Talks & Workshops *ML & Stats * in math

Aug 24

Linear Algebra Done Right *Linear Algebra * in math

Aug 23

ML Fragments *Research * in math

Jul 17

Introducing VAR-GPs *ML & Stats * in math

Jul 14

On model interpretability and explainability *Research * in math

Jul 10

An Introduction to Epipolar Geometry *Computer vision * in math

Jul 5

RL: Tricks of the Trade *Research * in math

Jun 11

Research Workshops *Research * in math

Jun 10

The Gaussian Cheatsheet *ML & Stats * in math

Mar 17

Machine Learning courses *ML & Stats * in math

Feb 2

Deriving the Cross Entropy Method *Reinforcement learning * in math

Jan 18

Topics in Bayesian Machine Learning *ML & Stats * in math

2019

Mar 17

The Stein Gradient *ML & Stats * in math

2018

May 21

Policy Gradients in a Nutshell *Reinforcement learning * in math

Feb 6

When Does Stochastic Gradient Algorithm Work Well? *ML & Stats * in math

2017

Dec 4

The beauty of Bayesian Learning *ML & Stats * in math

Oct 15

Visualizing the Confusion Matrix *ML & Stats * in math

Oct 8

The magic of Automatic Differentiation *ML & Stats * in math

Jul 18

A Primer on Projective Geometry *Computer vision * in math

Jun 24

Introduction to RANSAC *Computer vision * in math