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MLPR weekly activities

These pages say what you need to do each week. The background notes give an overview of the main assessed tasks and deadlines, including a suggested work pattern.

• Week 1 activities – Introduction to ML with Linear Regression
• Week 2 activities – ML fundamentals: generalization, error bars, Gaussians
• Week 3 activities – Classification and gradient-based fitting
• Week 4 activities – Bayesian linear regression
• Week 5 activities – Bayesian model choice and Gaussian processes
• Week 6 activities – More detailed models: Gaussian process kernels, more non-Gaussian regression
• Week 8 activities – Neural Networks
• Week 9 activities – Autoencoders, PCA, Netflix Prize
• Week 10 activities – Bayesian logistic regression, Laplace approximation
• Week 11 activities – Sampling-based approximate Bayesian inference, variational inference