Week 01

1. Bayesian inference, estimators and posterior summaries

2. Conjugacy

3. The beta-binomial model

Week 02

1. Bayesian linear regression

2. Model selection using the marginal likelihood

Week 03

1. Generative and discriminative classification

2. Logistic regression

3. Laplace approximations

Week 04

1. Covariance functions and the squared exponential kernel

2. Gaussian processes for regression

Week 05

1. Gaussian processes for classification

2. Non-gaussian likelihoods

Week 06

1. Multi-class classification

2. Decision theory

3. Calibration

Week 07

1. Markov Chain Monte Carlo Methods

2. Metropolis-Hasting algorithm

Week 08

1. MCMC and Convergence diagnostics

2. Gibbs sampling

3. Change point detection

Week 09

1. Variational inference (KL divergence and ELBO)

2. Bayesian formulation of the Gaussian mixture model

Week 10

Week 11

1. Black-box variational inference

2. Stochastic optimization

Week 12

1. The Erdös-Rényi model

2. The infinite relational model

Week 13

1. Regression modelling with heteroscedastic noise

2. Deep ensembles

3. Last-layer Laplace approximations