Summary statistics and visualisation for one variable

Summary statistics and visualisation for multiple variables

Testing population means with Z-tests and T-tests

Non-parametric estimation of probability distributions

Point estimates of probability distributions

Maximum Likelihood Estimation (MLE)

Maximum A-Priori (MAP) estimation

The Generalised Method of Moments (GMM)

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

Choosing parametric probability distributions

Dimensionality reduction with Principal Component Analysis (PCA)

K-means and k-mediods clustering

Bayesian parameter estimation of discriminative models

Point variable estimates for discriminative models

Using F-tests to compare regression models

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

The K-Nearest Neighbours (KNN) classifier

Confidence intervals of black box models

Imputing missing data for prediction

Multi-layer perceptrons and backpropagation

Additional link functions for neural networks

Alternatives to backpropagation

Neural networks and regression

Convolutional layers for neural networks

Autoencoders and Variational Autoencoders (VAE)

Restricted Boltzmann Machines (RBMs)

Classifying written characters

Ordinary Least Squares for inference

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

Imputing missing data for inference

Measurement error and inference

Heterogeneous treatment effects

Estimating Hidden Markov Models (HMMs)

Natural Language Processing (NLP)

Recurrent Neural Network (RNN) encoders and decoders