In this post I cover the fundamentals of Support Vector Regression. A high level summary is that an SVR model is regularised regression using the epsilon-insensistive loss function. The SVR objecti...
Support Vector Machines
In this post I cover the fundamentals of Support Vector Machines. A high level summary, is that a SVM is a linear classifier chosen such that the minimum distance from the decision boundary to any ...
Creating an R Package - Cheat Sheet
In this post, I cover the main commands that are used in the process of creating an R package. The majority of the commands are from the usethis and devtools R packages. Main references: The R ...
Gaussian Graphical Model
MVN Distribution [X \sim N(\bmu; \Sigma)] [\bmu]
First year PhD, machine learning notes
These are notes I have taken on classical machine learning methods and techniques. Basics of statistical learning Probabilistic graphical models - edit For the notes on pro...
Git Commands - Cheat Sheet
Git Setting up Already have local directory and want to create remote GitHub repository If you have a local directory you need to run git init to create a git repository. Then ...
Scheduling an R Script - GitHub Actions
Main references: I have used the links provided by this blog post by Simon Couch Used workflow templates from the rlib/actions repo Scheduling an R script with GitHub Actions I am working o...
Matrix Decompositions
References: Multivariate Statistics Lecture notes - by Shahin Tavokoli Choleski decompostion - blog post - notes Some very nice linear algebra blog posts Spectral Decomposition and SVD ...
Eigenvectors
Linear Algebra - Eigenvectors Eigenvalues and eigenvectors If $\mathbf{v}_i$ is an eigenvector of matrix $A$ with eigenvalue $\lambda_i$ and $\alpha \neq 0$, then any $\alpha \mathbf{v}_i$ is als...
Linear Algebra Foundations
Linear Algebra References: Linear algebra - lecture notes Fields A field is a set $S$ with two binary operations (a map from $S \times S$ to $S$ ): Addition and Multiplication. These two operatio...