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MLPR class notes

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Background information

Week 1: Introduction to ML with Linear Regression

Week 2: ML fundamentals: generalization, error bars, Gaussians

Week 3: Classification and gradient-based fitting

Week 4: Bayesian linear regression

Week 5: Bayesian model choice and Gaussian processes

Week 6: More detailed models: Gaussian process kernels, more non-Gaussian regression

Week 7: Reading week

Week 8: Neural Networks

Week 9: Autoencoders, PCA, Netflix Prize

Week 10: Bayesian logistic regression, Laplace approximation

Week 11: Sampling-based approximate Bayesian inference, variational inference

If you want to get a better idea of what we'll cover, and the style of the notes, last year’s notes are available. We'll cover mostly the same material, with some updates and a slightly different order, but we're changing the class activities and assessment. Please don't ask questions on last year's notes, but wait until they appear here.

 

A coarse overview of major topics covered is below. Some principles aren't taught alone as they're useful in multiple contexts, such as gradient-based optimization, different regularization methods, ethics, and practical choices such as feature engineering or numerical implementation.

You are encouraged to write your own outlines and summaries of the course. Aim to make connections between topics, and imagine trying to explain to someone else what the main concepts of the course are.