MLPR class notes

This is an archive of a previous version of the course. The 2018/19 notes are here.

You can step through the HTML version of these notes using the left and right arrow keys.

Each note links to a PDF version for better printing. However, if possible, please annotate the HTML versions of the notes in the forum, to keep the class's comments together. If the HTML notes don't render well for you, I suggest trying in Chrome/Chromium. If you want quick access to the PDFs from this page, you can toggle the pdf links.

A rough indication of the schedule is given, although we won’t follow it exactly.

Background information

w0a – Course administration, html, pdf.
w0b – Books useful for MLPR, html, pdf.
w0c – MLPR background self-test, html, pdf. Answers: html, pdf.
w0d – Maths background for MLPR, html, pdf.
w0e – Programming in Matlab/Octave or Python, html, pdf.
w0f – Expectations and sums of variables, html, pdf.

Week 1:

w1a – Course Introduction, html, pdf.
w1b – Linear regression, html, pdf.
w1c – Linear regression, overfitting, and regularization, html, pdf.

Week 2:

w2a – Training, Testing, and Evaluating Different Models, html, pdf.
w2b – Univariate Gaussians, html, pdf. Answers: html, pdf.
w2c – The Central Limit Theorem (CLT), html, pdf. Answers: html, pdf.
w2d – Error bars, html, pdf.
w2e – Multivariate Gaussians, html, pdf.

Week 3:

w3a – Classification: Regression, Gaussians, and pre-processing, html, pdf.
w3b – Regression and Gradients, html, pdf.
w3c – Logistic Regression, html, pdf.

Week 4:

w4a – Softmax and robust regressions, html, pdf.
w4b – Neural networks introduction, html, pdf.
w4c – More on fitting neural networks, html, pdf.

Week 5:

w5a – Backpropagation of Derivatives, html, pdf.
Wednesday guest lecture: John Quinn on malaria diagnosis. Jupyter notebook.

Week 6:

w6a – Autoencoders and Principal Components Analysis (PCA), html, pdf.
w6b – Netflix Prize, html, pdf.
w6c – Bayesian regression, html, pdf.

Week 7:

w7a – Bayesian inference and prediction, html, pdf.
w7b – Bayesian model choice, html, pdf.
A Bayesian linear regression demo: matlab/octave, python
w7c – Gaussian processes, html, pdf.
A minimal GP demo: matlab/octave, python
Alternative GP demo: matlab/octave, python

Week 8:

w8a – Gaussian Processes and Kernels, html, pdf.
w8b – Bayesian logistic regression and Laplace approximations, html, pdf.
w8c – Computing logistic regression predictions, html, pdf.

Week 9:

w9a – Variational objectives and KL Divergence, html, pdf.
w9b – More details on variational methods, html, pdf.
A minimal stochastic variational inference demo: Matlab/Octave: single-file, more complete tar-ball; Python version.
w9c – Gaussian mixture models, html, pdf.

Week 10:

w10a – Sparsity and L1 regularization, html, pdf.
w10b – More on optimization, html, pdf.
w10c – Ensembles and model combination, html, pdf.

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.

Linear regression and ML introduction
Evaluating and choosing methods from the zoo of possibilities
Multivariate Gaussians
Classification, generative and discriminative models
Neural Networks
Learning low-dimensional representations
Bayesian machine learning: linear regression, Gaussian processes and kernels
Approximate Inference: Bayesian logistic regression, Laplace, Variational
Gaussian mixture models
Time allowing: Other principles: sparsity/L1, ensembles: combination vs averaging.

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.