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Association rules
Summary statistics and visualisation for multiple variables
Privacy
Distance metrics and outliers
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
Dimensionality reduction with Principal Component Analysis (PCA)
K-means and k-mediods clustering
M-estimators
The Generalised Method of Moments (GMM)
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
Classification trees
Regression trees
Bayesian 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
Recommenders
Ordinary Least Squares for inference
Testing regression parameter estimates with Z-tests and T-tests
Multiple hypothesis testing
Generalised Least Squares
General Linear Models
Analysis of variance (ANOVA)
Instrumental Variables
Imputing missing data for inference
Measurement error and inference
Semi-parametric regression
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