MLPR 2016 | Notes | Log | Forum | Tutorials | Assignments | Feedback

**This is an archive of a previous version of the course.**
The 2017/18 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.
- w5b – Autoencoders and Principal Components Analysis (PCA), html, pdf.

Week 6:

- w6a – Netflix Prize, html, pdf.
- w6b – Bayesian regression, html, pdf.
- w6c – Bayesian inference and prediction, html, pdf.

Week 7:

- w7a – Bayesian model choice, html, pdf.
- w7b – Gaussian processes, html, pdf.
- w7c – Gaussian Processes and Kernels, html, pdf.
- A minimal GP demo: matlab/octave, python
- Alternative GP demo: matlab/octave, python

Week 8:

- w8a – Bayesian logistic regression and Laplace approximations, html, pdf.
- w8b – Computing logistic regression predictions, html, pdf.
- w8c – Variational objectives and KL Divergence, html, pdf.

Week 9:

- w9a – More details on variational methods, html, pdf.
- A minimal stochastic variational inference demo: Matlab/Octave: single-file, more complete tar-ball; Python version.
- w9b – Gaussian mixture models, html, pdf.
- Friday lecture: John Quinn on applications: materials.

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.

MLPR 2016 | Notes | Forum | Tutorials | Assignments | Feedback