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Multivariate probability

Exploratory data analysis

Association rules

Summary statistics and visualisation for multiple variables

Distance metrics and outliers

Estimating discriminative probability distributions

Bayesian parameter estimation of discriminative models

Point variable estimates for discriminative models

Using F-tests to compare regression models

Test sets and validation sets

Choosing parametric discriminative probability distributions

Unsupervised machine learning

Dimensionality reduction with Principal Component Analysis (PCA)

K-means and k-mediods clustering

Estimating generative probability distributions

The Generalised Method of Moments (GMM)

Supervised linear regression

Ordinary Least Squares for prediction

Regularising linear regression for prediction

Choosing linear models for prediction

Generalised linear models, the delta rule and binary classification

Generalised linear models and multiclass classification

Supervised machine learning

Classification trees

Regression trees

Support Vector Machines (SVMs)

Variational Bayes

The Naive Bayes classifier

The K-Nearest Neighbours (KNN) classifier

Discriminant analysis

Non-parametric regression

Ensemble methods

Ensemble methods for trees

Regularising black box models

Confidence intervals of black box models

Interpreting black box models

Semi-supervised learning

Imputing missing data for prediction

Linear regression for inference

Ordinary Least Squares for inference

Testing regression parameter estimates with Z-tests and T-tests

Multiple hypothesis testing

Generalised Least Squares

General Linear Models

Advanced inference

Analysis of variance (ANOVA)

Instrumental Variables

Imputing missing data for inference

Measurement error and inference

Semi-parametric regression

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Preface

This is a live document, and is full of gaps, mistakes, typos etc.